Scientists say they have uploaded a real genome to a quantum computer for the first time, marking an important step in applying the emerging technology to biology.

The researchers encoded the entire genome of the hepatitis D virus (HDV) onto a system powered by IBM's 156-qubit Heron quantum processing unit. This achievement came during the Quantum for Bio (Q4Bio) challenge, a competitive international research program designed to accelerate quantum computing applications for human health. The goal was to demonstrate that quantum computers could handle real-world genomic data in a format the machines could actually process.

A genome is naturally stored as a long sequence of letters (A, C, G, and T/U), whereas a quantum computer works with quantum states represented by qubits. Simply copying DNA letters into qubits is not enough; the information has to be transformed into a quantum representation that can be prepared, manipulated, and measured by the hardware.

The scientists with the Wellcome Sanger Institute converted the HDV genome into a quantum-compatible format, allowing quantum algorithms to analyze genetic information rather than just theoretical problems.

They said in a statement that they specifically targeted the most complex and variable genomes ‪—‬ tasks that can exceed the current capabilities of classical computers, including artificial intelligence (AI) systems.

Where quantum computing and biology intersect

"When we work with pangenomes, the information is presented in a form of a tangled maze, but we are building quantum algorithms to help find the best path through this maze when regular tools, such as classic computers, just get hopelessly stuck," said leader of the research team, Sergii Strelchuk, an associate professor at the Department of Computer Science at the University of Oxford.

"We’re aiming for a simple but game-changing idea by bringing quantum computing into the world of genomics."

The same researchers already demonstrated four key genomics capabilities on real quantum hardware within the same Q4Bio genomics project. They used data encoding to convert DNA sequences into a quantum-compatible format.

A step called sequence alignment mapped DNA fragments into reference genomes, while a process called pangenome assembly built genomes from multiple individuals' DNA data. They also used, phylogenetic tree construction to map evolutionary relationships among organisms.

The scientists chose HDV because it has a compact genome and is clinically relevant. Although its RNA folds into intricate secondary structures — rather than existing as a simple linear sequence — and it mutates rapidly (like many RNA viruses), HDV has one of the smallest known animal virus genomes — roughly 1,700 nucleotides of circular RNA.

It causes severe blood-borne liver infections through contact with infected bodily fluids, making it an ideal test case that balances complexity with practical biomedical importance, the team said.

Increasingly complex computations

The work also demonstrates that pangenomes — collections of genome sequences from many individuals of the same species — are where quantum computing truly shines. As more genomes join a pangenome, conventional computing resources can be overwhelmed due to combinatorial growth in complexity.

A pangenome is not just a collection of genomes stored side by side but a data structure that captures all the genetic variation across many individuals, strains, or populations. As more genomes are added, the amount of variation that must be represented, compared, and indexed grows rapidly.

Quantum machines may be better able to navigate this computational complexity because they can represent and process many possible genetic patterns at once in a way that might make certain large-scale comparison and search problems in genomics faster (or more efficient) than traditional computers.

In the future, faster and more powerful genomic analysis could let scientists rapidly track infectious diseases, improve their understanding of rare genetic disorders, and pinpoint disease-causing mutations, the team said. Loading the hepatitis D genome onto a quantum computer opens the door to solving biological problems that have been impossible for classical computers to tackle, James McCafferty, chief information officer at the Wellcome Sanger Institute, said in the statement.

Although the accomplishment is promising, practical applications may still be years away, Strelchuk and colleagues on the Q4Bio team said in the statement. The team wants to package these capabilities into a usable service that would allow the wider scientific community to upload data and choose between classical or quantum approaches (or both) to address computational challenges.

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A Chinese company has unveiled what its researchers are calling the world’s first "dual-core" quantum computer. It's a neutral-atom system designed to improve stability, efficiency and error correction by pairing two independent qubit arrays in a single machine.

The device, called "Hanyuan-2," is being promoted as a step toward more scalable quantum hardware. The Wuhan-based company CAS Cold Atom Technology announced the new machine in May, according to reports by ST Daily, a Chinese state media outlet, with technical details published on its website.

Gui-Guo Ge, a senior solutions expert at CAS Cold Atom Technology, the company behind the dual-core computer, told ST Daily that the system is built on independently controllable neutral-atom array technology. It works by conjoining two quantum arrays comprising a total of 200 qubits made from rubidium atoms (100 rubidium-87 atoms and 100 rubidium-85 atoms).

Ge added that the two cores are both complete arrays that can operate in parallel to boost computational efficiency or work in a "one main core and one auxiliary core" configuration to create more stable logical bits. That design is intended to address long-standing technical bottlenecks in single-core systems, including limited expansion and interference between neighboring qubits.

The dual-core architecture matters because quantum computers are notoriously fragile. Qubits are prone to "noise" in the form of small disturbances such as temperature fluctuations or electromagnetic interference, which can disrupt calculations. By splitting the system into two cooperating cores, Hanyuan-2 aims to reduce those problems by allowing the cores to correct each other's errors and divide tasks between them.

The setup offers a modular path to scaling up quantum processing units (QPUs), and the use of neutral atoms affords several advantages. For one, neutral atoms don't require massive dilution refrigerators that cool components to near absolute zero to function the way superconducting quantum computers, like those in use at IBM or Google machines do, meaning lower energy requirements.

Because neutral atoms are electrically neutral, they interact less with their environment than many other types of qubits, meaning qubits can, in theory, preserve quantum information for longer, with less decoherence — when calculations fail due to the collapse of superposition — and potentially improved error rates, providing longer coherence times.

Hanyuan-2 includes more than 500 optical tweezers arrays and a qubit lifetime of 100 seconds, according to the report. It also uses a standard rack-mounted design and needs only a small laser-cooling setup with power consumption below 7 kilowatts. This means it can be deployed in ordinary environments rather than specialized cryogenic facilities.

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Microsoft has revealed a new quantum computing chip with quantum bits (qubits) it says are capable of maintaining their quantum state for 1,000 times longer than its predecessor — paving the way for more reliable quantum computers by 2029. But not all scientists believe the company's claims.

The experimental quantum processing unit (QPU), dubbed Majorana 2, features a four-qubit array that offers a reported mean qubit lifetime of 20 seconds and, in some instances, up to a minute. This is a massive improvement in quantum coherence times — the time that qubits are entangled so that calculations can run in parallel — typically seen in QPUs. Normally, this lifetime is measured in milliseconds (thousandths of a second).

The new chip could put scientists on the path to building a quantum computer that's commercially viable by 2029 — halving the timespan researchers initially expected — Microsoft representatives said in a statement. The scientists who worked on the new processor outlined their findings in a June 2 preprint study, and the results have not yet been peer-reviewed.

"We need to make improvements each year that will get us closer to delivering a computer that we believe will have massive commercial and societal value," Chetan Nayak, Microsoft technical fellow, said in the statement. "We've got to keep marching to that roadmap to accomplish that, but where are we relative to last year? We’re 1,000 times better."

Despite the claimed progress against the first chip, Majorna 1, experts have called Microsoft's work in this specific niche of quantum computing research (called topological quantum computing) into question. They have previously questioned whether the underlying technology has yet been proven and have called for a wider evidence base for suggestions on qubit coherence times.

Despite the criticism, Microsoft representatives say this has halved the development time in building a future fault-tolerant quantum computer — a machine that can overcome errors and sustain long-duration calculations to potentially outperform supercomputers.

Next-generation topological qubits

The Majorana 2's predecessor was revealed in February last year. Both chips are based on a 90-year-old theory by Italian physicist Ettore Majorana that a particle could be its own antiparticle, meaning that it either annihilates itself in a massive release of energy or coexists stably when paired, enabling it to store quantum information as a qubit.

Because Majorana particles aren't found in nature, much of the research into them, including Microsoft's previous findings, centers on nudging them into existence.

Under the right conditions, the qubits in these chips can reach a "topological" state of matter — a specific phase in which atoms are entangled over long distances — which lets them tap into the laws of quantum mechanics to process the 1s and 0s of computing data in parallel.

Representatives said on the launch of Majorana 1 that these qubits were more stable, smaller, more scalable, and drained less power than qubits made from superconducting metals — like the ones commonly used in quantum computing systems made by companies like IBM, Google and Microsoft.

Qubits in the first Majorana chip consisted of a material stack combining a semiconductor made of indium arsenide (used in devices like night vision goggles) with an aluminum superconductor. This forms a "topoconductor," a topological superconductor whose qubits are stored in the shape of the material stack.

Each qubit is made from two superconducting nanowires ended by Majorana zero modes (MZMs) – the building blocks of topological qubits that store information through parity, evenness or oddness in the number of electrons in a topoconductor wire.

Instead of aluminum, Majorana 2 uses lead to shield fragile qubits from disturbances like electromagnetic waves or cosmic radiation. For the semiconductor, researchers swapped out indium arsenide for a combination of indium arsenide and indium arsenide antimonide. The change doubled the "topological gap" — the physical barrier that protects the qubits from environmental noise and errors during calculations.

It also led to a major increase in stability and reliability: boosting the quantum coherence lifetime from between 1 and 12 milliseconds in Majorana 1 to an average of 20 seconds (with a maximum lifespan of 1 minute), the researchers said in the study.

Combining AI and quantum computing

The key components of the Majorana 2 were designed atom by atom, so the scientists needed to add impurities in the form of other materials into the crystalline structure to lock each atom in its correct spot. But adding too many impurities, or adding them in the wrong way, would disturb the structure. To get these impurities into the right spots, the scientists turned to artificial intelligence (AI).

"Finding the exact recipe, the right amount to put to get the desired energy structure, requires a lot of experimentation in the old world order. In the new world order, through simulations, you can see where the highly probable target is. And then with that knowledge, you ideally only have to experiment once,” Zulfi Alam, corporate vice president for quantum at Microsoft, said in the statement.

Using the Microsoft Discovery platform, the scientists deployed AI agents to keep track of the complex intersectional elements while designing Majorana 2 — with changes to any of the software, architecture, design, the materials stack, the fabrication processes, measurements, and others, carrying ramifications for every other element. The project also had close to two decades' worth of data in many different formats, which were stuck in different silos. But AI agents were able to resynthesize the data and establish connections between the different pieces of information.

AI also slashed the time it took to conduct experiments from weeks by "several orders of magnitude," Alam said in the statement, but did not specify the exact time saving.

"Using agentic AI to automate the measurements was a game changer,” said Alam said in the statement. "It goes through some math and starts saying, '"Hey, where do I find the lowest point where everything sort of works?'" And it can do all these voltage adjustments in parallel, which a human cannot do. The way our minds work, we are more linear."

Pathway to the holy grail

Nayak said in a technical blog post that the company is now cutting its timeline to build a practical and scalable quantum computer in half with a new target of 2029. "This achievement will mark a major milestone on the path to a transformative fault-tolerant quantum computer that has the potential to solve problems that affect all of humanity."

This timeline sits roughly in line with competitors in the field. But this apparent progress in the field of topological quantum computing is not without its detractors.

Following the release of Majorana 1 last year, physicists questioned the extent to which Microsoft researchers proved that MZMs were present in the device. Nayak, who was involved in last year's research, later presented additional evidence at a talk at the Global Physics Summit in March.

Others have criticized the evidence for the claims made in the new study. Speaking with Scientific American, scientists including Sergey Frolov, a quantum computing researcher at the University of Pittsburgh, suggested that the data reported has yet to be proven credible. Frolov cites the fact that Microsoft's last preprint of this kind was unpublished, meaning it wasn't peer-reviewed

Speaking with Live Science, Yuval Boger, quantum computing researcher and chief commercial officer at QuEra, a quantum computing company that is building neutral atom machines, lauded the progress but urged caution.

"Topological qubits are a bold, long-horizon bet, and the device improvements they reported are worth noting," he said. "As with any announcement of this kind, the sensible thing is to wait for peer review and independent reproduction before drawing conclusions," he added.

"The community has debated the topological evidence since 2018, and that scrutiny is healthy for everyone," he said. "It's also worth keeping the news in proportion. Topological computing has not yet demonstrated a working qubit, while other modalities are considerably further along."

Competing entities, including companies and research institutions, are working on a host of different qubit modalities as they all strive to hit the holy grail of building a fault-tolerant quantum computer that exponentially scales down its errors as you increase the size of the system. This is known as "below threshold" quantum error correction. They may include superconducting qubits, neutral atom qubits, photonic qubits, or, in Microsoft's case here, topological qubits, among others.

"In the end, any real progress in quantum computing is good for all of us," he said. "The field moves fastest when many approaches are pushing at once, and we welcome that."

Researchers have developed a method to reduce uncertainty in artificial intelligence (AI) systems by tapping into the power of quantum computers. They say their work represents the first demonstration of "quantum enhancement" in a production-scale, pretrained large language model (LLM).

One of the key metrics used to measure the quality and capabilities of AI systems such as Anthropic's Claude, OpenAI's ChatGPT and similar services is a unit known as "perplexity" — often expressed as PPL. This measures a system's general ability to properly predict the next word in a sentence or sequence of words.

A system with a low PPL is considered better at predicting the next word, while one with a high PPL is mathematically more likely to produce erratic outputs. There are multiple methods to reduce PPL in large AI models, including fine-tuning, training on larger datasets, and adding parameters.

GPT-5.5, for example, is estimated to have somewhere between 2 trillion and 5 trillion parameters. In all standard LLMs, each parameter takes up space in the system’s memory, meaning that as these models become larger and more capable, they require increasingly larger infrastructure.

But scientists at Multiverse Computing have found an alternative to scaling up the infrastructure around AI. In a new study uploaded May 7 to the arXiv preprint database, they proposed that a relatively small boost in the number of parameters in an AI model can lead to a significant reduction in perplexity when running them using quantum circuit blocks — the fundamental units of quantum computations.

"The results reported here constitute, to our knowledge, the first demonstration of end-to-end quantum enhancement of a production-scale, widely-deployed LLM on real superconducting quantum hardware for autoregressive language generation," the scientists wrote in the study. "Their significance lies not in the magnitude of the perplexity improvements — which will grow with hardware fidelity and qubit count — but in the fact that they exist at all."

A step forward for quantum-enhanced AI

In the study, the researchers created and executed quantum circuit blocks called Cayley-parameterized unitary adapters (CUAs).

Cayley parameters are a set of mathematical matrices that can be "trained" by weighting them towards specific matrix components. They’re inserted into a specific layer of an LLM for training on a classical computer.

The LLM's original parameters are frozen during this process so that they remain unchanged. The new hybrid system containing both the trained Cayley parameters and the original model parameters is then executed on the 156-qubit IBM Quantum System Two superconducting quantum processing unit (QPU).

The resulting quantum-classical hybrid model lowered the perplexity of Llama 3.1 8B — an 8 billion-parameter model created by Meta — by 1.4% while adding only 6,000 parameters (a 0.000075% increase).

Borja Aizpurua, a senior research scientist at Multiverse Computing and first author of the study, described the new technique as a proof of concept for further development. Speaking with Live Science, he explained that quantum computers can provide some advantages over a strictly classical paradigm — but they come with a trade-off.

"The first thing you do is encode [the parameters] in the quantum computer. Once you have encoded the state, you are ready to apply the Cayley unitary adapter, which we train classically and then implement in quantum hardware," he said.

He explained that these adapters are small, which is important because the bigger the circuit, the more "noise" there is. Noise generated during quantum computations — which can come from interactions with nearby qubits, disturbances from the Earth’s magnetic field, radiation from Wi-Fi or phones, and even cosmic rays — may cause errors and render outputs and measurements meaningless.

As in much of quantum computing research, quantum error correction is one of the main areas of interest. In this study, mitigating errors caused by noise was the primary obstacle Aizpurua and the Multiverse Computing team were attempting to overcome.

Tackling real-world problems

The scientists loaded the classically trained Cayley unitary adapters into the quantum system before end-to-end inference — the phase of AI use where the model executes a response — occurred. Then, the hybrid outputs could be measured against the normal non-quantum-enhanced results.

The researchers discovered that the hybrid model could answer several questions correctly that the base Llama model could not.

In one astronomy question, the original model incorrectly selected an answer indicating that only Saturn has Jovian planet rings. However, the CUA-enhanced model correctly identified all jovian planets as ringed.

In another example, the original model incorrectly answered a biology question on the population-genetic consequences of gene flow, selecting “Hardy–Weinberg disruption” while the CUA-enhanced model correctly identified increased genetic homogeneity.

"So here we can see an example in which a model doesn't answer correctly, and then you add something quantum and suddenly it answers correctly," Aizpurua said.

This result, coupled with the measured 1.4% reduction in perplexity, demonstrates a clear path forward for developing quantum hybrid AI systems, Aizpurua said. He added that this research could help researchers overcome current development bottlenecks where systems are constrained by developers' ability to scale classical computing infrastructure.

Future research would involve developing methods by which the entire quantum circuit, not just the Cayley unitary adapters, is directly encoded, Aizpurua said. This would ostensibly result in an LLM capable of achieving lower perplexity and higher accuracy, using fewer parameters than any purely classical method.

Ultimately, he said, the goal of the research is to produce higher-quality AI systems capable of reaching "quantum advantage," a term that describes a quantum-based computer system capable of performing feats unachievable by any classical computer.

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Researchers have created a new type of "quantum operation" that is dramatically more stable than previous methods. The achievement brings one hardware design, in particular — neutral‑atom qubits — a step closer to powering useful quantum computers.

Quantum computers use qubits that can exist in a state of 0, 1 or a superposition of both. Key to their processing power are "gates" capable of shuffling qubits between those states so they can run calculations in parallel. One critical type of gate is called a swap gate, which allows information to be routed through a machine by exchanging two qubits' states.

Many quantum systems rely on highly excited electronic states or collisions between atoms, as well as on the tunnel effect, in which particles slip through obstacles that would be impassable according to classical physics. However, swap gates that use those techniques (particularly the tunnel effect) are subject to how quickly lasers — which suspend neutrally charged atoms in place to form the qubits — can be turned on and how powerful they are.

This means that tiny fluctuations in the timing or strength of a laser could introduce errors and a lack of fidelity into the system, making a gate unreliable.

It feeds into the major bottleneck preventing scientists from scaling up quantum computing so they can be more powerful than the world's fastest supercomputers: qubits are highly susceptible to sustaining errors and breaking down during calculations. This rate is roughly 1 in 1,000 versus 1 in 1 trillion for conventional bits.

To resolve this issue, scientists at ETH Zurich devised a way to make qubits in neutral-atom quantum computers far more stable than ever before. They outlined their findings in a study published April 8 in the journal Nature.

Opening the gateway to more stable quantum computers

Rather than relying on conventional gates, the team used a subtler physical effect called a geometric phase. Unlike other methods for implementing quantum gates for neutral atoms or trapped particles, which depend on how fast and hard atoms are pushed, their swap gate exploits the path the atoms take through an artificial "crystal of light" built by intersecting laser beams (called an optical lattice).

Neutral‑atom platforms promise thousands of qubits in a single device. This setup uses tens of thousands of potassium atoms cooled to near absolute zero and held in place by laser light. Yann Hendrick Kiefer, a postdoctoral researcher at the ETH Zürich Institute for Quantum Electronics and first author of the study, told Live Science how this works.

"Laser light is nothing but monochromatic electromagnetic radiation," Kiefer said in an email. "If a neutral atom is placed inside this electric field a dipole moment is induced which leads to a force that enables us to hold atoms in place."

When two of those potassium atoms are brought close enough that their quantum waves overlap, their combined state changes in a way that depends only on the geometry of their motion, not on how quickly they move or how intense the lasers are. This makes the swap operation far less sensitive to experimental noise.

"Quantum mechanics is described by wave functions," Kiefer said. "Manipulation of this wavefunction generally introduces a phase on the wavefunction, which can be either of dynamical or geometric origin."

Dynamical quantum methods create this phase based on highly precise control over things like energy levels, timing, and laser strength, which means even tiny mistakes can cause errors. The geometric approach works differently: instead of depending on exact timing or force, it depends mainly on the overall path the system takes from start to finish. Because of that, it’s naturally less sensitive to outside disturbances or small imperfections, making these quantum operations more stable and reliable.

Building machines that will need far fewer qubits than we thought

Using this method, the research team achieved a very robust swap gate with a precision of better than 99.91%, operating in under a millisecond (one-thousandth of a second) across a system with a remarkable 17,000 qubit pairs. While some superconducting or trapped‑ion gates can be sub‑microsecond (one-millionth of a second), those systems typically run such gates on only a handful of qubit pairs at once.

The team also proved that they were capable of creating "half-swap" gates, which are critical for running real quantum algorithms. Half‑swap gates — a quantum operation that only swaps two qubits partway instead of completely — are vital because entanglement is the special ingredient in quantum computing. A full swap mostly just moves information around, but a half-swap can both partially exchange information, and create correlations between qubits that classical bits can't have. The scientists hope to eventually pair these robust swaps with a quantum gas microscope — which can image and target individual atom pairs — to build a more flexible, programmable quantum computing architecture.

That said, Kiefer admits a practical quantum computer is still way off. "Quantum computing on a practical scale still requires significant advancements," he said. "The most limiting factors are twofold: scale and fidelity."

However, Kiefer remains optimistic. He cited a recent study that explored how we could one day solve complex problems like Shor's algorithm with a system that uses as few as 10,000 qubits, rather than the millions we previously assumed we would need.

Shor's algorithm is a quantum recipe that can quickly crack certain kinds of modern encryption by finding the secret prime‑number ingredients of a big number faster than a classical computer can, and it remains a widely used benchmark in quantum computing research.

"There is a lot of work to be done before actually solving Shor's algorithm," Kiefer said, "but we are entering the phase in which the dream of quantum computing might actually be slowly converted into reality — exciting times!"

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Researchers have created a network that they say demonstrates the real-world feasibility of a quantum internet that's physically impossible to hack, at least without detection.

Working with quantum startup Qunnect and networking company Cisco, the scientists connected a trio of nodes across New York's existing fiber-optic cables with quantum signals in the form of photons (packets of light), where quantum states are used to carry information through entangled qubits. By distributing and swapping entanglement between the signals, the scientists effectively connected them into a small quantum network.

The demonstration builds on work in 2023, in which the same team connected a pair of nodes between Brooklyn and Manhattan. The addition of a third node shows that it's possible to use existing physical infrastructure to create something approaching a true quantum network, the scientists say. They outlined their findings in a study uploaded Feb. 17 to the arXiv preprint database.

This third node acts as an intermediate hub where the team could perform entanglement swapping and routing, turning two links into a small multi‑node quantum network that can distribute entanglement across different pairs on demand. This would act more like a true network rather than a single line.

"Manhattan is a very compact place," said Javad Shabani, director of NYU's Center for Quantum Information Physics and the NYU Quantum Institute. "Everything is within five or six miles, and you can find hundreds of financial institutions in a very small radius. That density — of infrastructure, institutions, and potential users — may make the city one of the first places where a quantum internet begins to take shape. Having this network right now is important. It's a huge investment that will pay off probably in the next decade or so."

A blueprint for future quantum networks

A quantum internet is deemed "unhackable" due to device-independent quantum key distribution (DI-QKD), a method by which cryptography keys are encoded in the quantum state of particles such as photons. It's not possible to copy quantum states, and measuring them disturbs them — meaning that eavesdropping is difficult and simple to detect.

Information travels via photons, but they can get easily lost in fiber. In addition, "noise" — disturbances caused by the environment or other stimuli — scrambles their states, thus limiting data transfers to very short distances.

To extend this range, the team created a "hub-and-spoke" network — an intermediary hub for swapping and routing with two outlying spokes. To accomplish this, they created simple nodes at Qunnect's Brooklyn facility and generated pairs of photons that are entangled — meaning their quantum states are linked so they share information over space and time. These flowed across 5 to 6 miles (8 to 10 kilometers) of deployed commercial fiber to a central hub at a QTD Systems facility, a commercial data center and network facility in Lower Manhattan.

One vital advance came in the form of "entanglement swapping" — a process by which particles that have never previously interacted can become entangled. This is key for building short connections into a larger network, the scientists said.

This relies on measurements that "transfer" entanglement from initial pairs to distant ones. It relies on quantum teleportation — the phenomenon where two or more particles share linked quantum states — so measuring one instantaneously determines correlated properties of the others. However, instead of teleporting data between two entangled qubits, it teleports the state of entanglement itself.

The swapping happened at the QTD center, where cryogenic detectors ‪—‬ ultra-sensitive photon detectors cooled to extremely low temperatures to reliably detect single photons carrying quantum information ‪—‬ measured the photons and linked pairs that had never interacted. The result was city-spanning entanglement between the original outer sources.

Addressing the internet's Achilles' heel

Conventional data transfers are highly susceptible to eavesdropping. Scientists say the quantum internet would solve this issue because any interception disturbs the photons, making the tampering immediately apparent.

This experiment proves metropolitan-scale quantum links work with live telecom fibers, solving the issues of weakening or loss of photons as they travel through optical fiber cables, alongside temperature extremes and vibration that can wreck fragile entanglement.

The hub-and-spoke design addresses scalability by centralizing complex cryogenic gear at one hub. This sidesteps the issue of every node requiring pricey, power-hungry cooling, meaning the network can be expanded without ballooning costs.

In the short term, this demonstration paves the way for QKD, the sharing of unhackable encryption keys to protect sensitive data from sources like banks, the government or the healthcare industry.

In the longer term, it's a step toward true distributed quantum computing, which could link multiple devices to address highly sophisticated problems, like drug discovery or climate modeling, that no single operator could handle.

Entangled networks could also be deployed to boost quantum sensing, which could lead to ultraprecise clocks, navigation without GPS and other high-precision sensor arrays.

Among the key challenges are that fiber-optic cables absorb and scatter photons exponentially with length — about 0.2 decibels per kilometer at telecom wavelengths — dropping entanglement success to near zero beyond 62 miles (100 km) without boosting. The new experiment transmitted information over a mere 5 to 6 miles (8 to 10 km) per leg; spanning longer distances will require quantum repeaters, which lack the quantum memories required to function effectively.

However, the experiment was important in proving the viability of quantum networks outside a strictly controlled laboratory environment. The scientists showed that the effects of noise and loss can be adequately managed to sustain entanglement across a dense metropolis like New York.

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Researchers have created the world's first miniature, proof-of-concept quantum battery. If the technology can be replicated, it could transform the field of energy storage forever and open up new possibilities for lightweight, remote electrification, experts say.

The research team outlined their design for the quantum battery in a study published March 13 in the journal Light: Science & Applications. They say it can be used for long-term battery storage, as well as high-density battery applications such as heavy electric vehicles.

In the future, quantum batteries could charge far faster than traditional batteries, as well as demonstrate far higher energy density and durability, said James Hutchinson, co-author of the study and an associate professor of Physical Chemistry at the University of Melbourne.

In a standard lithium-ion (Li-ion) battery, ions move between the cathode and the anode through an electrolyte. But inside a quantum battery, energy is stored as electromagnetic excitation among coherent molecules — molecules that share non-random internal states such as their vibrational energy or electron states. This allows them to maintain a fixed relationship with one another.

Quantum batteries rely on the weird laws of quantum mechanics. In this case, the researchers relied on quantum coherence — an effect in which a mass of local particles exists in multiple states at once. These particles, though in a "superposition" of states, act in predictable ways relative to one another. Collected in the battery, the coherent particles undergo quantum entanglement, which means they are not simply aligned with one another but functionally the same, forming one larger system.

This allows all molecules within the battery to charge at a constant speed, no matter its size. The more molecules involved, the more efficiently energy is absorbed throughout the system, meaning charging times actually decrease in real terms as the battery size increases.

"Similar to conventional batteries, quantum batteries charge, store and discharge energy,", explained Hutchinson in the statement. "But while everyday batteries rely on chemical reactions, quantum batteries leverage properties of quantum mechanics. The advantage of quantum is that the system absorbs light in a single, giant 'super absorption' event and this charges the battery faster."

Composition of the quantum battery

To build the battery, the researchers relied on the Dicke model in quantum optics, which states that when light and matter are coupled beyond a set value, they can become superradiant — where a group of emitters emit light collectively in a short, intense pulse.

In practical terms, the battery is made up of a series of organic semiconductor layers (where the coupling occurs) sandwiched between silver mirrors, creating a microcavity — a microscopic structure that confines light to a small volume, allowing it to reflect multiple times.

This allows the coherent group of molecules or atoms to emit light in a unified pulse — a necessary function for the discharging of the quantum battery — as well as to absorb light at a rate equal to the number of coherent molecules squared. This is known as superabsorption. The microcavity is essential for coupling and superabsorption, as it provides the right confined environment to achieve the set ratio between light and matter set out in the Dicke model.

Beneath and above the organic semiconductors, hole blocking and electron transport layers ensure electrons can flow toward the cathode and electrodes when necessary so the system can actually function as a battery.

In tests at the University of Melbourne's Ultrafast and Microspectroscopy Laboratories, the researchers fired a laser pulse with a bandwidth of 31 nanometers for a femtosecond (one-quadrillionth of a second), which prompted an excited state in the molecules for tens of nanoseconds (several hundred millionths of a second).

This means the battery is capable of holding a charge for 1 million times longer than the time it takes to charge it.

On this scale, a battery that took one minute to charge could remain charged for "a couple of years," first researcher James Quach, science leader at CSIRO, Australia's national science agency, told The Guardian.

Going forward, the researchers aim to scale up the battery in such a manner while retaining its charge. This is a key hurdle, as the energy stored in quantum batteries is susceptible to environmental noise, which can disrupt or eliminate quantum behavior in a process known as decoherence.

If this obstacle can be overcome, the implications of a practical quantum battery could be profound. For instance, remote charging via lasers could open up more opportunities for batteries in drones or aircraft because they could be charged in midair.

Andrew White, who leads the Quantum Technology Laboratory at the University of Queensland, told The Guardian that an initial application could be to power quantum computers at a very low energy cost.

Researchers have demonstrated a breakthrough method for preventing errors in light-powered quantum computers before they even occur.

The milestone, which was achieved using a new technique called photon distillation, means physicists are one step closer to developing light-based “photonic” quantum computers capable of achieving quantum advantage over classical supercomputers.

In a study uploaded Jan. 9 to the arXiv preprint database, scientists detailed a "net-positive" method for mitigating errors in photonic quantum computers.

The research tackles what is arguably the biggest hurdle in the path to developing fault-tolerant universal quantum computers, the presence of noisy errors that can cause computations to fail.

Unlike superconducting quantum computers, which leverage electronic circuits to create qubits — the quantum equivalent of computer bits — photonic quantum computers are powered by light. Scientists shoot beams of photons (units of light) through specifically engineered fields of mirrors and beam splitters. The photons themselves are then manipulated into complex quantum states that allow computations to be performed.

One of the key benefits of this quantum computing paradigm is that it works at room temperature. The underlying reason this is possible is also the culprit behind photonic quantum computing's biggest problem: photonic quantum computers can operate without generating much excess heat because light is in constant motion. This motion allows computations to occur through the interactions between photons as they move. But it also produces significantly more errors.

The fault tolerance problem

Superconducting quantum computers have to energize circuits to create qubits ‪—‬ a process that generates heat. Although photons don't suffer from this problem, there's a trade-off: photonic quantum computers are very brittle. Photons are, by their very nature, imperfect, which means there's typically a significant percentage of "bad" photons bouncing around that can ruin a given computation.

"Because photons are moving at the speed of light, you have qubits that are constantly moving through the system," Jelmer Renema, chief scientist and co-founder of QuiX Quantum, told Live Science. "And the way that computations work is by interactions between these photons when they encounter each other on the chip."

"Errors occur when one of the photons doesn't play nice," Renema said. "Every once in a while, there's sort of a maverick photon that decides to not play by the rules of the other photons."

This "rogue" photon will work its way through the system without ever interacting with the other photons, producing a distinct error. Because this happens before the photon is even turned into a qubit for processing, this problem is difficult to address through conventional quantum error correction, which typically involves techniques to address qubit errors after they've occurred.

Using a technique called quantum photonic distillation, QuiX employed error mitigation to tackle the root cause of these errors before they could happen.

"You set up the interference in such a way that the probability that your rogue photon makes it to the output … is lower than the probability that the photons that are playing nice make it to that output," Renema said.

This probability lies at the heart of photonic quantum computing. As Renema put it, "Everything in photonics is probabilistic." When researchers shoot beams of photons through a series of mirrors and beam splitters, there's a certain probability that each photon will do what it wants, and if nothing is done to mitigate errors, they're essentially relying on luck to produce viable computations.

The odds of success get even worse for each photon as engineers add more quantum computing gates to the system.

Below the threshold

With a superconducting quantum computer, you can add "logical" qubits to perform fault tolerance on physical qubits to compensate for errors. These are collections of physical qubits that share the same data, so that if one or more qubits fail, the data is available elsewhere in the cluster and calculations are not disrupted. But with quantum computing, adding overhead tends to produce more errors than it fixes.

Photonic distillation also exhibits "below threshold error mitigation" — a metric the study authors used to indicate that their technique reduces the number of errors that occur as the system scales, as opposed to adding more, which is normally the case as you make a quantum computer bigger, the QuiX scientists wrote in the study.

Similar fault tolerance milestones have been achieved in superconducting and neutral-atom quantum computers. Google achieved below-threshold error correction in its Willow quantum processing unit (QPU) in December 2024, for example. But the new study represents the first time this has been achieved in light-powered systems.

"The amount of qubits that you need to expend in order to make a single good qubit is so enormous that the cost of the computer just blows up enormously," Renema said. "So there's this trade-off."

Photonic distillation sends imperfect photons through a specialized optical circuit that uses "quantum interference" — a strange feature of quantum mechanics wherein the probability amplitudes of quantum states combine — to filter out physical inconsistencies and output a single, high-quality photon. All of this happens before the photons are turned into qubits.

These high-quality photons are then sent through the system with a much lower probability of going rogue. This quality increase provides a net gain in error correction even when taking into account all the errors introduced when the photons are used as qubits.

Because photonic computers are probabilistic, this experimental work demonstrates a scalable approach to error mitigation that should provide below-threshold performance at scales great enough to produce useful quantum computations, the study authors said.

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Computer scientists say they've developed a new encryption method designed to defend sensitive data from one of the biggest looming threats in cybersecurity: quantum computers powerful enough to crack today's cryptographic systems.

In a study published Feb. 2025 in the journal IEEE Transactions on Consumer Electronics (but publicized in a statement March 2, 2026), the researchers proposed a hybrid encryption framework specifically designed to protect video data — everything from surveillance footage to video calls — from both current hackers and future quantum-powered attacks.

Quantum computers are widely expected to transform fields such as chemistry and advanced materials, but the same technology could also create headaches for cybersecurity. Much of the encryption protecting bank transactions, private messages and secure websites depends on mathematical problems that would take today's fastest supercomputers millions to billions of years to solve. A sufficiently powerful quantum machine, however, could solve those problems in hours or days, potentially exposing data that is currently considered secure.

"Think of a regular computer hack as someone trying to pick a traditional door lock ‪—‬ it could take days, even years, to try every combination," S.S. Iyengar, a professor and director of the Digital Forensic Center of Excellence at Florida International University, said in the statement. "But a quantum computer hack is like having a key that could try multiple combinations simultaneously. This is what makes quantum threats so powerful."

Quantum-proof encryption, frame by frame

To tackle that problem, the researchers focused on how video is encrypted and transmitted over the internet. Their system combines conventional security techniques with elements designed to remain resilient even if quantum computing advances. Instead of encrypting video as a single large file, the method generates pseudorandom keys that scramble individual frames before transmission.

In practical terms, the video data is encrypted using cryptographic keys that only authorized users can decode. Even if attackers intercept the transmission, the underlying information remains unreadable without the correct key.

What makes the technique different from conventional approaches is its focus on the video's structure. Video files often contain patterns — repeated structures created by compression algorithms or frame similarities — that attackers can sometimes exploit during cryptanalysis, the practice of finding weaknesses in cryptographic algorithms. The new framework tries to eliminate those patterns by increasing the randomness, or "entropy," of encrypted video frames.

According to the study, this statistical randomness is a key factor in how encryption strength is measured. In their simulations, the researchers measured factors such as how random the scrambled data appeared and how closely neighboring data points resembled each other. The more random the output, and the fewer detectable patterns it contained, the harder it would be for attackers to analyze.

Based on those tests, the team said the system outperformed similar video encryption methods by about 10% to 15% in their simulations. The gains came mainly from stripping away patterns that attackers sometimes use as clues when analyzing encrypted files.

Another important aspect of the design is that it runs on today's conventional computers. While the system is designed with future quantum computing threats in mind, it doesn't require specialized quantum hardware. That means it could theoretically be integrated into existing infrastructure that's currently used for video conferencing, cloud storage or surveillance systems.

Safeguarding against Q-Day

This new technique is only one piece of a much larger effort to prepare for "Q-Day" — the hypothetical future moment when quantum computers achieve supremacy and become powerful enough to break widely used encryption systems. Governments and industry groups around the world are already working to replace vulnerable cryptographic standards with quantum-resistant alternatives.

The push to prepare for quantum-era security is already underway. The U.S. National Institute of Standards and Technology has spent years evaluating new forms of encryption designed to survive attacks from future quantum machines, for instance. The agency is currently standardizing several of those algorithms so they can eventually replace the public-key systems used across the internet today.

The new research doesn't replace those emerging standards. Rather, it represents a complementary layer of protection tailored specifically to video data. As video communication becomes more central to business, government and everyday life — and as synthetic media and deepfakes become easier to create — it is increasingly important to ensure that video streams remain authentic and secure, experts say.

The researchers are working to scale the system beyond small test files to full-length video streams and real-time communication platforms. If successful, the technology or a similar system could eventually be used to protect everything from corporate meetings to surveillance networks against both present-day hackers and future quantum computers.

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Researchers have achieved a new record for qubit fidelity in superconducting quantum computer systems — overcoming a key barrier in quantum computing.

In a study published Feb. 27 in the journal Nature Communications, scientists from IBM, RWTH Aachen University in Germany and Los Angeles-based startup Quantum Elements addressed quantum error correction and suppression, which is the largest hurdle to building machines more powerful than the fastest supercomputers.

Superconducting quantum computers use quantum bits (qubits), the quantum equivalent of a computer bit, to perform computations. The systems the researchers used — IBM's 127-qubit Kyiv and Marrakesh processors — employ a combination of "physical qubits" and "logical qubits," groups of entangled physical qubits that store the same information in different places, in case a physical qubit storing that information fails mid-calculation.

Physical qubits are embedded in a quantum computer's hardware layer as a complex, geometrically precise circuit made of superconducting metal. When cooled to near absolute zero, these metals lose all electrical resistance, allowing quantum information to flow without losing energy.

But these qubits are susceptible to the slightest perturbation, including vibration, local background noise and other environmental factors, making them brittle by nature. To compensate for this fragility, scientists group multiple physical qubits together to form a logical qubit.

When computations are performed across logical qubits, the physical qubits act as parity bits that eliminate errors. But the inherent problem with this setup, the scientists said in the new study, is that it's weak against "logical errors."

Logical errors occur when multiple physical qubits within a logical qubit succumb to noise. Essentially, when one physical qubit fails, the others act as a fail-safe against its erroneous signal. But when multiple qubits fail, the system treats the error they produce as the proper signal — and the calculation is ruined.

Suppressing errors before they happen

The 127-qubit IBM systems the researchers used are prone to a specific type of noise called "ZZ crosstalk," which is generated by the particular arrangement of its physical qubits.

The Quantum Elements team developed a hybrid approach to dealing with this specific type of noise. It involves suppressing crosstalk errors before they happen, thus reducing the overall number of undetectable logical errors that can occur. They coupled this technique with existing error-correction tools to create a novel hybrid protocol.

As a result, the researchers achieved the highest-fidelity quantum calculations ‪—‬ those with the lowest amount of noise ‪—‬ on superconducting qubits for the longest period of time on record.

According to the study, scientists had previously achieved a peak encoding fidelity of 79.5% in one attempt and 93.7% in another, which subsequently declined to approximately 30% after roughly 27 microseconds.

The peak-fidelity metric indicates the highest accuracy achieved within the quantum system, which occurs directly after the logical qubit's formation. The longer a quantum computer can hold peak or near-peak fidelity, the more capable it is at running quantum algorithms.

The team shattered those previous records, using a new technique called normalizer dynamical decoupling (NDD). They achieved 98.05% peak encoding fidelity, which maintained 84.87% fidelity after 55 microseconds.

Conventional dynamical decoupling, a standard error-correction technique, involves using microwave pulses to force physical qubits to flip back and forth. This regulates the qubits and generally averages out background noise, but it does so one physical qubit at a time.

But there's a problem with scaling up this technique: the more physical qubits there are in a system, the more microwave pulses you need to suppress the noise. Eventually, this creates additional noise and adds even more errors to the system, defeating the purpose, the study authors explained.

However, the scientists applied this paradigm to the logical qubit layer, rather than running it strictly at the hardware layer. To do this, they had to invent a method for tuning its pulses, using a mathematical "normalizer" based on the quantum code running on the machine itself. This allowed it to pulse in a rhythm correlating with the machine's code.

The result, normalizer dynamical decoupling, produced the highest-fidelity calculations on a superconducting quantum computer to date. The longer this level of high fidelity can be maintained, the more useful we can expect quantum computers to become.

The number of quantum gates — or single quantum operations — a quantum system can execute depends on how long it can maintain quantum fidelity. It typically takes about 10 to 12 nanoseconds for a single gate to execute. This means approximately 4,500 to 5,500 consecutive operations could occur in the 55 microseconds before the data degrades, as demonstrated in this study.

The ultimate goal of quantum computing is to create a device that can run at high fidelity long enough to perform truly useful operations, such as running Shor's algorithm to crack encryption. It's estimated that advanced functions such as these could one day take weeks or months for a capable quantum system to complete properly — which isn't that bad when you consider that it could take a classical computer hundreds of trillions of years to achieve the same result.

The record-breaking 55 microseconds of high-fidelity activity seems a far cry from achieving utility, but it represents a significant leap over previous efforts.

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Quantum computers don't need to be nearly as powerful as we thought to break the world's most secure encryption algorithms, scientists warn.

New research claims that quantum computers can make widely used cryptographic security systems obsolete with far fewer quantum bits, or qubits, than scientists have widely predicted ‪—‬ leaving sensitive data, like banking information and private messages, thought to be protected by encryption, open to interception.

Quantum computers run calculations in parallel, rather than in sequence, meaning that increasing the number of qubits that power them exponentially boosts their performance. Theoretically, this means the machines could one day solve calculations in seconds that would take the fastest supercomputers millions of years to complete.

One example of such a calculation is Shor's algorithm. This quantum algorithm, designed in 1994 by mathematician Peter Shor, can efficiently factorize large numbers. It was the first evidence that quantum computers could theoretically outperform classical computers in a practical problem.

Because it is virtually unbreakable by classical means, it has become the basis for RSA public-key encryption, which is behind many of the world's leading encryption schemes.

Scientists previously assumed that you would need a system with millions of qubits to break Shor's algorithm using a quantum computer — a far cry from today's best processors, which have just hundreds of qubits. But now, a surprising new study uploaded March 31 to the arXiv preprint database warns it could be viable to solve this algorithm with a system that has just 10,000 qubits.

Worse yet, the authors argue that a quantum computer with just 26,000 qubits could take as little as seven months to crack RSA-2048 encryption, the industry encryption standard used to protect most digital certificates on the internet.

Building error-free quantum computers

The reason behind this shift from needing a system with millions of qubits to just tens of thousands comes down to improvements in the field of quantum error correction (QEC) and the increased robustness of neutral-atom quantum computers, the scientists said.

Unlike classical bits, qubits are inherently "noisy," meaning they have a much higher error rate — 1 in 1 million million versus 1 in 1,000. This makes qubits far more likely to fail during calculations, with scientists saying that future systems need millions of qubits to outpace classical computers, rather than the hundreds of qubits fitted into today's state-of-the-art systems.

One method of reducing error rates is to use logical qubits. These are collections of entangled physical qubits that share the same data, meaning that if one of the constituent physical qubits fails, the data exists elsewhere and calculations may continue running uninterrupted.

QEC projects aim to engineer qubits and software layers that make quantum computers less prone to errors, meaning fewer qubits are needed in a fault-tolerant system to achieve comparable performance levels.

Neutral-atom quantum computers, meanwhile, are powered by qubits that are individual, charge-neutral atoms (normally, elements like rubidium, cesium or ytterbium) held in suspension by focused laser beams (known as optical tweezers) and cooled to near absolute zero.

Neutral-atom quantum computers are an alternative to conventional superconducting qubits used in the processors made by major companies like IBM, Microsoft and Google, and the study authors cited these systems as prime candidates for fault-tolerant quantum computing due to QEC advances.

Specifically, physical qubits can participate in many logical qubits, not just one, theoretically cutting the number of qubits needed for one logical qubit from hundreds or thousands to as few as five.

"Recent neutral-atom experiments have demonstrated universal fault-tolerant operations below the error-correction threshold, computation on arrays of hundreds of qubits, and trapping arrays with more than 6,000 highly coherent qubits," the scientists wrote in the study, which has not been peer-reviewed yet.

"Although substantial engineering challenges remain, our theoretical analysis indicates that an appropriately designed neutral-atom architecture could support quantum computation at cryptographically relevant scales," they added. "More broadly, these results highlight the capability of neutral atoms for fault-tolerant quantum computing with wide-ranging scientific and technological applications."

Solving the toughest encryption algorithms

In the study, the scientists proposed several new architectures for fault-tolerant quantum computers and analyzed performance with different error-correction mechanisms.

Existing neutral-atom machines with 500 qubits, as well as 6,000-qubit arrays, have both demonstrated "below-threshold" operation. This means that once you apply QEC, increasing the number of qubits exponentially reduces the error rate — so the bigger the system is, the more error-correction compounds to render the quantum computer fault-tolerant. This is the opposite of applying no error-correction techniques, where error rates exponentially rise as you increase the qubit count in a quantum computer.

In the study, the researchers extrapolated the potency of existing quantum computing systems and projected how powerful they would need to be to pose a threat to our cryptographic systems. They examined three key cryptographic algorithms: Shor's algorithm, which is now a benchmark for quantum computing performance; ECC-256,a modern-but-less-complex form of cryptography that's used to secure internet traffic and protect cryptocurrency; and the widely-used RSA-2048.

They indicated in the study that, with no error correction applied, state-of-the-art quantum computers would need 1 million qubits to crack RSA in one week, while ECC would require only 500,000 qubits and tens of minutes to solve.

Based on the calculations in the study, Shor's algorithm would be solvable with a system fitted with just 11,961 qubits. A system with between 10,000 and 26,000 qubits could crack ECC-256 within 10 days, and a machine with between 11,000 and 14,000 qubits could solve RSA-2048 in under three years.

The researchers also predicted that parallelized architectures with approximately 102,000 qubits would crack RSA-2048 encryption in 97 days.

Although future quantum processors with thousands of logical qubits "will unlock a wide variety of applications with significant scientific and economic value," the scientists wrote, these findings suggest we must take urgent measures to shift away from standard encryption. Google engineers, for example, say the world has less than three years to migrate to post-quantum cryptography.

It's worth noting that the study focused only on current QEC, leaving the door open to smaller systems achieving the same feats should other techniques improve. The scientists pointed out that improved physical qubit fidelities — designing physical qubits that are inherently less error-prone by nature — or algorithmic compression — further reducing the physical qubits required — are among the breakthroughs likely to be achieved in the coming years — meaning halving the number of qubits needed in future encryption-busting systems.

"These findings have significant implications. Although substantial expertise, experimental development effort, and architectural design are required, our theoretical analysis suggests that a neutral atom system capable of implementing Shor's algorithm could be constructed," they wrote. "This conclusion underscores the importance of ongoing efforts to transition widely-deployed cryptographic systems toward post-quantum standards designed to be secure against quantum attacks."

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A powerful quantum chemistry engine is now available that can help scientists tackle complex chemical problems. The new technology could drastically speed up research in drug discovery, materials science and other fields, the system's developer, QDX, claims.

The Extreme-scale Electronic Structure System (EXESS) can perform more than 1 quintillion calculations per second to address questions in quantum chemistry, QDX representatives said in a statement.

Quantum chemistry calculations play a major role in the development of new medicines and materials. For example, researchers use quantum chemistry simulations to understand how drugs interact with molecular binding sites in the body. That understanding can help researchers modify the drug molecule to optimize the speed and efficiency of that binding.

But traditionally, the modeling of quantum chemistry "takes up an absolutely mammoth amount" of computing power, Loong Wang, CEO of QDX, told Live Science. "It's actually, in many situations, genuinely faster to synthesize a compound and test it over the course of several weeks than to try and do a calculation on that compound."

The amount of computing power needed scales exponentially with the number of atoms in the system. Accurately solving problems with large molecules such as proteins, which can contain thousands of atoms, quickly becomes untenable. The purpose of EXESS, Wang said, is to "make quantum chemistry actually fast enough to use in practice."

EXESS operates 3,000 to 4,000 times faster than many other quantum chemistry software packages, QDX says, opening up calculations with large molecules like proteins. There's no single innovation driving that huge increase, and it runs on conventional hardware — so no quantum computing is needed. Instead, Wang and his colleagues optimized many individual components of the software, which together increase the speed and scale of the computations.

One way the team sped up calculations was by finding ways to run multiple operations at the same time. Many quantum chemistry algorithms are designed to operate in sequential steps. But even with extraordinary computing power, "nine chefs can't cook a recipe in one-ninth of the time," Wang said. The team found ways to alter the algorithms or theoretical approaches to enable more processes to be run in tandem, like "an industrial kitchen where you're just cranking out recipes," Wang added.

For example, the team implemented a technique known as molecular fragmentation, which breaks down a problem into smaller fragments, computes those fragments at the same time, and then stitches those pieces back together. That enabled them to speed up large calculations by running many smaller calculations at once.

"There are calculations that would, in principle, take about a month that actually take closer to 12 minutes" when run using EXESS, Wang told Live Science.

QDX is currently focused on using EXESS for drug discovery, finding and optimizing interactions between medicines and the body or better understanding how existing drugs function and why people develop resistances to them. But the company is offering free access for approved research projects. A limited version of the software is also available to the general public.

"I hope that people do stuff that we're not presently doing, and I don't mean that from a competitive standpoint," Wang said. "We have a couple of problems that we're choosing to focus on that we think are really interesting. But what we really want to see is people focus on the other 99% of problems that exist, and see what they do with it, and see whether in some of those areas, we might be surprised at how quantum chemistry can help make a difference."

Physicists at Silicon Quantum Computing have developed what they say is the most accurate quantum computing chip ever engineered, after building a new kind of architecture.

Representatives from the Sydney-based startup say their silicon-based, atomic quantum computing chips give them an advantage over other kinds of quantum processing units (QPUs). This is because the chips are based on a new architecture, called "14/15," that places phosphorus atoms in silicon (named as such because they are the 14th and 15th elements in the periodic table). They outlined their findings in a new study published Dec. 17 in the journal Nature.

SQC achieved fidelity rates between 99.5% to 99.99% in a quantum computer with nine nuclear qubits and two atomic qubits, resulting in the world’s first demonstration of atomic, silicon-based quantum computing across separate clusters.

Fidelity rates measure how well error-correction and mitigation techniques are working. Company representatives say they have achieved a state-of-the-art error rate on their bespoke architecture.

This might not sound as exciting as quantum computers with thousands of qubits, but the 14/15 architecture is massively scalable, the scientists said in the study. They added that demonstrating peak fidelity across multiple clusters serves as a proof-of-concept for what, theoretically, could lead to fault-tolerant QPUs with millions of functional qubits.

The secret sauce is silicon (with a side of phosphorous)

Quantum computing is performed using the same principle as binary computing — energy is used to perform computations. But instead of using electricity to flip switches, as is the case in traditional binary computers, quantum computing involves the creation and manipulation of qubits — the quantum equivalent of a classical computer’s bits.

Qubits come in numerous forms. Google and IBM scientists are building systems with superconducting qubits that use gated circuits, while some labs, such as PsiQuantum, have developed photonic qubits — qubits that are particles of light. Others, including IonQ, are working with trapped ions — capturing single atoms and holding them in a device referred to as laser tweezers.

The general idea is to use quantum mechanics to manipulate something very small in such a way as to conduct useful computations from its potential states. SQC representatives say their process for doing this is unique, in that QPUs are developed using the 14/15 architecture.

They create each chip by placing phosphorus atoms within pure silicon wafers.

"It's the smallest kind of feature size in a silicon chip," Michelle Simmons, CEO of SQC, told Live Science in an interview. "It is 0.13 nanometers, and it's essentially the kind of bond length that you have in the vertical direction. It's two orders of magnitude below typically what TSMC does as its standard. It's quite a dramatic increase in the precision."

Increasing tomorrow’s qubit counts

In order for scientists to achieve scaling in quantum computing, each platform has various obstacles to overcome or mitigate.

One universal obstacle for all quantum computing platforms is error correction (QEC). Quantum computations happen in extremely brittle environments, with qubits sensitive to electromagnetic waves, temperature fluctuations and other stimuli. This causes the superposition of many qubits to "collapse," and they become unmeasurable — with quantum information lost during calculations.

To compensate, most quantum computing platforms dedicate a number of qubits to error mitigation. They function in a similar way to check or parity bits in a classical network. But as qubit counts increase, so too does the number of qubits required for QEC.

"We have these long coherence times of the nuclear spins and we have very little what we call "bit flip errors." So, our error correction codes themselves are much more efficient. We're not having to correct for a bit flip and phase for errors,” Simmons said.

In other silicon-based quantum systems, bit flip errors are more prominent because qubits tend to be less stable when manipulated with coarser accuracy. Because SQC’s chips are engineered with high precision, they’re able to mitigate certain occurrences of errors experienced in other platforms.

"We really only have to correct for those phase errors," added Simmons. "So, the error correction codes are much smaller, therefore the whole overhead that you do for error correction

is much, much reduced."

The race to beat Grover’s algorithm

The standard for testing fidelity in a quantum computing system is a routine called Grover’s algorithm. It was designed by computer scientist Lov Grover in 1996 to demonstrate whether a quantum computer can demonstrate "advantage" over a classical computer at a specific search function.

Today, it’s used as a diagnostic tool to determine how efficiently quantum systems are operating. Essentially, if a lab can reach quantum computing fidelity rates in the range of 99.0% and above, it’s considered to have achieved error-corrected, fault-tolerant quantum computing.

In February 2025, SQC published a study in the journal Nature in which the team demonstrated a 98.9% fidelity rate on Grover’s algorithm with its 14/15 architecture.

In this regard, SQC has surpassed firms such as IBM and Google; although they have shown competitive results with dozens or even hundreds of qubits versus SQC’s four qubits.

IBM, Google and other prominent projects are still testing and iterating their respective roadmaps. As they scale up the qubit count, however, they’re forced to adapt their error mitigation techniques. QEC has proven to be among the most difficult to overcome bottlenecks.

But SQC scientists say their platform is so "error deficient" that it was able to break the record on Grover’s without running any error correction on top of the qubits..

"If you look at the Grover's result that we produced at the beginning of the year, we've got the highest fidelity Grover album [algorithm] at 98.87% of the theoretical maximum and, on that, we're not doing any error correction at all," Simmons said.

Simmons says the qubit "clusters" featured in the new 11-qubit system can be scaled to represent millions of qubits — although infrastructure bottlenecks may yet slow down progress..

"Obviously as we scale towards larger systems, we are going to be doing error correction," said Simmons. "Every company has to do that. But the number of qubits we will need will be much smaller. Therefore, the physical system will be smaller. The power requirements will be smaller."

Scientists have developed a new fabrication method for creating superconducting quantum bits (qubits) that could remain coherent for three times longer than current state-of-the-art systems in labs — allowing them to conduct more powerful quantum computing operations.

The new technique, described in a study published Nov. 5 in the journal Nature, relies on the use of a rare earth element called tantalum. This belongs to the "transition metals" group of the periodic table and is "grown" on minerals such as tantalite and silicon by building up a metallic film atom-by-atom.

Researchers used tantalum grown on silicon to create qubits capable of remaining coherent for up to 1.68 milliseconds. This is roughly three times longer than the coherence times reported in a lab setting, and up to 15 times longer than in the superconducting qubits used by the likes of Google and IBM in their quantum processing units (QPUs), the scientists said in a statement.

"The real challenge, the thing that stops us from having useful quantum computers today, is that you build a qubit and the information just doesn’t last very long," said Andrew Houck, Princeton’s dean of engineering and co-principal investigator of the study, in the study. "This is the next big jump forward."

Decoherence and imperfection

Coherence in quantum computing is a measure of how long a qubit can maintain its wave state. When qubits decohere, they lose information. This makes maintaining coherence one of the biggest challenges in quantum computing.

Scientists have spent some years trying to harness tantalum as a material to develop qubits. When a superconducting material such as tantalum is cooled to near absolute zero, circuits built within the material can operate with close to no resistance. This allows for faster quantum operations, but the speed and number of operations are fundamentally limited by how long qubits can maintain their information states.

An advantage of tantalum is that it’s easier to scrub free of contaminants that can lead to imperfections in the manufacturing process, where any irregularity can cause affected qubits to decohere faster. Tantalum’s inert resilience protects it from certain state changes related to corrosion and molecular displacement; it won’t even absorb acid when immersed. This makes it a perfect candidate for use as a superconducting material for quantum computing, the scientists said in the study.

But keeping the qubit material free from defects is only half the battle. The manufacture of a quantum processor requires both a base layer material and a substrate. In previous experiments, scientists achieved state-of-the-art quantum computing results using processors built with a tantalum base layer and a sapphire substrate. These experiments were successful, but coherence rates were still under one millisecond.

The Princeton team replaced the sapphire substrate used in those experiments with a high-resistivity silicon developed using proprietary techniques. According to the study, they achieved coherency rates as high as 1.68 milliseconds on systems as large as 48 qubits — marking an all-time best for superconducting qubits.

The new qubit design is similar to those used in superconducting quantum processors developed by leading companies such as Google and IBM. Houck even added that "swapping Princeton’s components into Google’s best quantum processor, called Willow, would enable it to work 1,000 times better."

What this means for the quantum computing industry remains unclear. While the scientists have progressed the coherence rates of qubits significantly, challenges remain. Chief among them is the availability of tantalum. As of 2025, tantalum is considered a scarce metal with most mining taking place in Africa.

While the new qubits significantly increase coherence, they still need to be tested at larger sizes using wafer-scale chipsets before they can be integrated with today’s commercially deployed quantum computers.

Scientists say they've developed a breakthrough 3D wiring solution that allows a 100-fold increase in the number of quantum bits (qubits) a quantum computing chip can support.

Typical quantum computing processors (QPUs) are built with two-dimensional, horizontal wiring, just like the central processing units (CPUs) in our classical devices. But this traditional wiring limits the number of qubits scientists can cram onto a given processor. Currently available chips from Google and IBM, for example, contain approximately 105 qubits and 120 qubits, respectively.

The new architecture, called VIO-40K, overcomes this limitation by using three-dimensional, vertical wiring, according to representatives of QuantWare, which developed the technology. The VIO-40K architecture supports 40,000 input-output (I/O) lines and is made up of fully integrated chiplet modules connected via "ultra-high-fidelity chip-to-chip connections," QuantWare representatives said in a statement.

This adds up to a single QPU capable of supporting 10,000 simultaneous qubits — a 100-times increase over the current state of the art in superconducting quantum computers — on a smaller chip. This is the first time such a qubit count has been achieved on a single quantum processor, according to QuantWare.

"For years, people have heard about quantum computing's potential to transform fields from chemistry to materials to energy, but the industry has been stuck at 100-qubit QPUs, forcing the field to theorize about interesting but far-off technologies," Matt Rijlaarsdam, CEO of QuantWare, said in the statement. "QuantWare's VIO finally removes this scaling barrier, paving the way for economically relevant quantum computers. With VIO-40K, we're giving the entire ecosystem access to the most powerful, hyper-scaled quantum processor architecture ever."

Vertical integration meets quantum democratization

QuantWare representatives say they expect to start shipping the first VIO-40K units in 2028. To support this target, the firm says it will build an industrial-scale QPU fabrication factory in Delft, Netherlands, which is scheduled to open in 2026. This will be "one of the world's largest quantum fabs" and the first dedicated fab for quantum open architecture (QOA) devices.

To put this timeline into perspective, IBM's current quantum computing development roadmap puts the arrival of 2,000-qubit QPUs at 2033 or beyond, with no time frame set for chips capable of supporting 10,000 qubits.

The bottleneck, for most firms working on superconducting quantum computers, lies in the way quantum processors are built. Because fabricators can only squeeze so many wires onto a single wafer, physicists have to chain multiple processors together. While the connections between the qubits on each chip are high-fidelity, the connections between the chips themselves are often low-fidelity, causing a bottleneck for data transmission.

QuantWare's VIO series uses vertical wiring that purportedly allows as many as 10,000 qubits to fit on a chip that is smaller than today's 100-qubit wafer-style chips. This is accomplished through the use of "chiplet" technology that involves stitching together individually fabricated modules to form complete chips.

Instead of relying on low-fidelity chip-to-chip connections as current quantum processors do, chiplets are fabricated separately and then sealed together to create a system-on-a-chip environment capable of functioning as a single QPU.

A quantum brain in a box

QuantWare's timeline is relatively ambitious compared with its peers', but representatives say one factor working in the company's favor is its adoption of QOA.

Unlike Google and IBM, QuantWare isn't developing an end-to-end quantum computing solution. Its QPUs are built to work with components from other firms, such as Qblox controllers and Nvidia software.

This means the VIO-40K will essentially be plug-and-play with Nvidia NVQLINK — an architecture designed to allow QPUs to connect with GPUs in a hybrid classical-quantum system — thus allowing it to interface with existing supercomputers. This will also let it connect with Nvidia CUDA — a parallel computing platform and programming model — to enable developers to seamlessly integrate entire quantum workloads into the hybrid systems.

Ultimately, this puts QuantWare in the position to potentially act as an Intel-like hardware provider for quantum computing systems, working with other quantum computing entities in the process.

In today’s digital age, silicon is king. But as with other semiconductors that are widely used in the industry, trace quantities of other elements are often added to silicon to influence its electronic behaviour, a process known as doping.

Now, scientists have taken doping to a new level, replacing one in every eight atoms in germanium — a semiconductor similar to silicon – with the superconductor gallium, so that the material forms a new superconductor that can be used for technologies like quantum computing and sensing.

Although silicon is next in line for this approach, germanium is already widely used in industry and is extremely compatible with silicon. The researchers outlined their approach in a new study published Oct. 30 in the journal Nature Nanotechnology.

"I think there is a lot of good reasons to be excited about this," co-author of the study Javad Shabani, a professor of physics at New York University, told Live Science.

The idea of doping a semiconductor enough to render it superconducting was first proposed in 1964 by Marvin Cohen, Professor Emeritus at the University of California, Berkeley, then at the University of Chicago. The idea was resuscitated in the 2000s and 2010s, when several groups attempted to bombard silicon and germanium with superconducting metals to see if they could achieve the theoretically predicted new phase — but they hit problems.

"When you bombard, you kind of ruin the lattice," Shabani explained, adding that you then need to heat it up and "anneal" it to run further experiments for superconducting behaviour, so it is not clear whether dopant atoms have simply formed an island of superconducting material, or whether a new superconducting phase has formed in the bombarded element. He and his team even tried the experiments themselves. "We just added to the puzzle," he told Live Science.

Layer of hope

Progress finally came when they switched to a technique called molecular beam epitaxy. Here they produced the germanium crystal layer by layer, by exposing the surface to germanium atoms with just the right conditions and concentration of gallium atoms for one of the gallium atoms to substitute in for a germanium atom in each unit cell of the crystal.

Shabani suggested they were likely not alone in thinking molecular beam epitaxy might be worth a try. However, attempts had been discouraged by a lot of negative speculation suggesting that doping to the required levels was not physically possible based on assumptions akin to solubility limits. For example, you can keep dissolving more and more sugar in water up to a point, but once you reach the solubility limit, the solution saturates and the sugar will no longer dissolve but remain in solid lumps. Transfer the same arguments to doping and you might think that beyond a certain limit, the dopant will not evenly distribute either but clump together.

But doping by molecular beam epitaxy is a different kind of process altogether — the two materials are laid down together — so it is not limited by anything akin to a solubility limit. "We are just spraying something on something," said Shabani, adding that no laws are violated.

To check what they had, Shabani and his team sent their samples to colleagues at the University of Queensland in Australia to characterize them with their state-of-the-art equipment. As Julian Steele, a researcher at the University of Queensland in Australia who helped with the characterization experiments, pointed out, usually "the precision required" to characterize the interesting superconducting layer buried in the bulk germanium would be experimentally "intractable."

"It was a fortunate combination of well-defined crystal layers and very precise measurements that worked in tandem to produce data with atomic-level precision," Steele told Live Science in an email. "The result is an undeniably clear picture of a new and fascinating quantum material."

The researchers also noted that the superconducting transition temperature was 3.5 Kelvin (just above absolute zero) — cryogenically cold, but not as cold as the 1 Kelvin required to achieve superconductivity in pure gallium. As Shabani highlighted, normally you would expect the transition temperature to be even lower than that of the "parent" superconductor, in this case gallium. This throws some intriguing questions out as to which of the known mechanisms for superconducting behaviour is at play here.

"It is very satisfying to see continued research with successes in the field of superconductivity in doped semiconductors, which I initiated over sixty years ago," Cohen told Live Science in an email. "I believe that there is still much to be learned about superconductivity through research on systems of this kind."

Building more robust qubits

Peter Jacobson, a University of Queensland researcher who also helped with the characterization experiments, was particularly impressed by "how clearly the distortion emerged."

He pointed out that the spacing of the atoms in the plane of each deposited crystal layer remained essentially unchanged from the pure germanium seed layer, but that the spacing perpendicular to this plane increased slightly, just as would be expected to accommodate the slightly larger gallium atoms. "Seeing this behaviour so clearly is a strong indication of how little disorder is present in these films."

That low disorder is good news for anyone seeking to “grow” alternating layers of semiconducting and superconducting material, something which has not been possible before.

This drastically increases the device density that can be achieved on a wafer, because it means you can build up into 3D stacks. Shabani uses the example of a Josephson junction — a junction of a non-superconducting material sandwiched between superconducting material either side. These can be used in quantum sensing and for qubits in quantum computing.

"You can fit 25 million of these on one wafer," he said. He points out that currently each Josephson Junction is around a millimetre in size and added: "Each of these could be a qubit. It could be a pixel of a sensor, right?"

The close adherence to regular crystalline order may have additional benefits for protecting against “decoherence” of superconducting qubits. When qubits decohere, they are no longer capable of holding multiple values at once but lump for a definite value and essentially respond as a classical qubit without the advantage of quantum behaviour.

This is a bugbear in efforts towards quantum computing, but it has been suggested that some of this decoherence may be associated with amorphous characteristics in the materials used. Further experiments will be needed for verification, but the improved crystallinity in these molecular beam epitaxy gallium-doped germanium structures may help qubits to be more robust against decoherence.

What is quite clear is the potential advantage of using the fabrication methods that already exist to make germanium and silicon semiconductor computer processors and devices.

"You have a trillion-dollar silicon germanium infrastructure that now can use superconductivity as a new item in their toolbox,” said Shabani. "That may really help solid-state quantum computing — the timeline could really shrink."

Quantum computers are coming. And when they arrive, they are going to upend the way we protect sensitive data.

Unlike classical computers, quantum computers harness quantum mechanical effects — like superposition and entanglement — to process and store data in a form beyond the 0s and 1s that are digital bits. These "quantum bits" — or qubits — could open up massive computing power.

That means quantum computers may solve complex problems that have stymied scientists for decades, such as modeling the behavior of subatomic particles or cracking the "traveling salesman" problem, which aims to calculate the shortest trip between a bunch of cities that returns to its original destination. But this massive power also may give hackers the upper hand.

"Like many powerful technologies, you can use [quantum computing] for great good," Rebecca Krauthamer, a technological ethicist and CEO of cybersecurity firm QuSecure, told Live Science. "And you can also use it for malicious purposes."

When usable quantum computers first come online, most people — and even most large organizations — will still rely on classical computers. Cryptographers therefore need to come up with ways to protect data from powerful quantum computers, using programs that can run on a regular laptop.

That's where the field of post-quantum cryptography comes in. Several groups of scientists are racing to develop cryptographic algorithms that can evade hacking by quantum computers before they are rolled out. Some of these cryptographic algorithms rely on newly developed equations, while others are turning to centuries-old ones. But all have one thing in common: They can't be easily cracked by algorithms that run on a quantum computer.

The foundations of cryptography

Cryptography dates back thousands of years; the earliest known example is a cipher carved into ancient Egyptian stone in 1900 B.C. But the cryptography used by most software systems today relies on public key algorithms. In these systems, the computer uses algorithms — which often involve factoring the product of two large prime numbers — to generate both a public key and a private key. The public key is used to scramble the data, while the private key, which is available only to the sender, can be used to unscramble the data.

To crack such cryptography, hackers and other malefactors often must factor the products of very large prime numbers or try to find the private key by brute force — essentially throwing out guesses and seeing what sticks. This is a hard problem for classical computers because they have to test each guess one after another, which limits how quickly the factors can be identified.

A 100-story skyscraper on a three-story building

Nowadays, classical computers often stitch together multiple encryption algorithms, implemented at different locations, such as a hard disk or the internet.

"You can think of algorithms like building bricks," Britta Hale, a computer scientist at the Naval Postgraduate School, told Live Science (Hale was speaking strictly in her capacity as an expert and not on behalf of the school or any organization.) When the bricks are stacked, each one makes up a small piece of the fortress that keeps out hackers.

But most of this cryptographic infrastructure was built on a foundation developed in the 1990s and early 2000s, when the internet was much less central to our lives and quantum computers were mainly thought experiments. "It's like a foundation for a three-story building, and then we built a 100-story skyscraper on it," Michele Mosca, co-founder and CEO of cybersecurity company evolutionQ, told Live Science. "And we're kind of praying it's OK."

It might take a classical computer thousands or even billions of years to crack a really hard prime factorization algorithm, but a powerful quantum computer can often solve the same equation in a few hours. That's because a quantum computer can run many calculations simultaneously by exploiting quantum superposition, in which qubits can exist in multiple states at once. In 1994, American mathematician Peter Shor showed that quantum computers can efficiently run algorithms that will quickly solve prime-number factoring problems. As a result, quantum computers could, in theory, tear down the cryptographic fortresses we currently use to protect our data.

Post-quantum cryptography aims to replace obsolete building blocks with less-hackable bricks, piece by piece. And the first step is to find the right math problems to use. In some cases, that means returning to equations that have been around for centuries.

Currently, the National Institute of Standards and Technology (NIST) is looking at four problems as potential foundations for post-quantum cryptography. Three belong to a mathematical family known as structured lattices. These problems ask questions about the vectors — mathematical terms that describe direction and magnitude between interconnected nodes — like the connection points in a spiderweb, Mosca said. These lattices can theoretically have an infinite number of nodes and exist in multiple dimensions.

Experts believe lattice problems will be hard for a quantum computer to crack because, unlike some other cryptographic algorithms, lattice problems don't rely on factoring massive numbers.

Instead, they use the vectors between nodes to create a key and encrypt the data. Solving these problems may involve, for example, calculating the shortest vector in the lattice, or trying to determine which vectors are closest to one another. If you have the key — often a "good" starting vector — these problems may be relatively easy. But without that key, they are devilishly hard. That's because no one has devised an algorithm, like Shor's algorithm, that can efficiently solve these problems using quantum computing architecture.

The fourth problem that NIST is considering belongs to a group called hash functions. Hash functions work by taking the virtual key for unlocking a specific point on a data table, scrambling that key and compressing it into a shorter code. This type of algorithm is already a cornerstone of modern cybersecurity, so in theory, it should be more straightforward to upgrade classical computers to a quantum-proof version compared with other post-quantum cryptographic schemes, Mosca said. And similarly to structured lattices, they can't easily be solved by brute force alone; you need some clue as to what's going on inside the "black box" key generator to figure them out within the age of the universe.

But these four problems don't cover all of the potentially quantum-safe algorithms in existence. For example, the European Commission is looking at an error-correcting code known as the McEliece cryptosystem. Developed more than 40 years ago by American engineer Robert McEliece, this system uses random number generation to create a public and private key, as well as an encryption algorithm. The recipient of the private key uses a fixed cipher to decrypt the data.

McEliece encryption is largely considered both faster and more secure than the most commonly used public-key cryptosystem, called Rivest-Shamir-Adleman. As with a hash function, would-be hackers need some insight into its black-box encryption to solve it. On the plus side, experts consider this system very safe; on the downside, even the keys to unscramble the data must be processed using extremely large, cumbersome matrices, requiring a lot of energy to run.

A similar error-correcting code, known as Hamming Quasi-Cyclic (HQC), was recently selected by NIST as a backup to its primary candidates. Its primary advantage over the classic McEliece system is that it utilizes smaller key and ciphertext sizes.

Another type of algorithm that sometimes comes up in conversations about post-quantum cryptography is the elliptic curve, Bharat Rawal, a computer and data scientist at Capitol Technology University in Maryland, told Live Science. These problems go back at least to ancient Greece. Elliptic curve cryptography exploits basic algebra — calculating the points on a curved line — to encrypt keys. Some experts believe a new elliptic curve algorithm could evade hacking by a quantum computer. However, others argue that a hacker could hypothetically use Shor's algorithm on a quantum computer to break most known elliptic curve algorithms, making them a less-secure option.

No silver bullet

In the race to find quantum-safe cryptographic equations, there won't be a silver bullet or a one-size-fits-all solution. For example, there's always a trade-off in processing power; it wouldn't make much sense to use complex, power-hungry algorithms to secure low-priority data when a simpler system might be perfectly adequate.

"It's not like one algorithm [combination] will be the way to go; it depends on what they're protecting," Hale said.

In fact, it's valuable for organizations that use classical computers to have more than one algorithm that can protect their data from quantum threats. That way, "if one is proven to be vulnerable, you can easily switch to one that was not proven vulnerable," Krauthamer said. Krauthamer's team is currently working with the U.S. Army to improve the organization's ability to seamlessly switch between quantum-safe algorithms — a feature known as cryptographic agility.

Even though useful (or "cryptographically relevant") quantum computers are still several years away, it is vital to start preparing for them now, experts said. "It can take many years to upgrade existing systems to be ready for post-quantum cryptography," Douglas Van Bossuyt, a systems engineer at the Naval Postgraduate School, told Live Science in an email. (Van Bossuyt was speaking strictly as a subject-matter expert and not on behalf of the Naval Postgraduate School, the Navy or the Department of Defense.) Some systems are tough to upgrade from a coding standpoint. And some, such as those aboard military craft, can be difficult — or even impossible — for scientists and engineers to access physically.

Other experts agree that post-quantum cryptography is a pressing issue. "There's also the chance that, again, because quantum computers are so powerful, we won't actually know when an organization gets access to such a powerful machine," Krauthamer said.

There's also the threat of "harvest-now, decrypt-later" attacks. Malicious actors can scoop up sensitive encrypted data and save it until they have access to a quantum computer that's capable of cracking the encryption. These types of attacks can have a wide range of targets, including bank accounts, personal health information and national security databases. The sooner we can protect such data from quantum computers, the better, Van Bossuyt said.

And as with any cybersecurity approach, post-quantum cryptography won't represent an end point. The arms race between hackers and security professionals will continue to evolve well into the future, in ways that we can only begin to predict. It may mean developing encryption algorithms that run on a quantum computer as opposed to a classical one or finding ways to thwart quantum artificial intelligence, Rawal said.

"The world needs to keep working on this because if these [post-quantum equations] are broken, we don't want to wait 20 years to come up with the replacement," Mosca said.

Scientists at IBM have created two new quantum processing units (QPUs) that they say will take them a step closer to achieving quantum advantage by next year — and a fully fault-tolerant quantum computer by 2029.

The first processor, called IBM Quantum Nighthawk, is a 120-qubit chip that can process quantum calculations that are 30% more complex than anything the company's previous QPU (R2 Heron) could handle.

The company also launched another processor, IBM Loon, with 112 qubits, which scientists say includes all the elements required for full fault tolerance — quantum computers that self-detect and correct all errors in real time.

New quantum processors

Nighthawk enables each of the 120 qubits in the processor to connect with its nearest four neighbors in a square lattice structure, thanks to 218 improved tunable couplers — components that govern connections between individual qubits on the chip. This represents a 20% improvement in the number of couplers in the previous Heron processor.

This architecture will enable scientists to explore problems that require 5,000 two-qubit gates — fundamental entangling operations required for quantum computations.

According to IBM representatives, the company hopes that future versions of Nighthawk will be able to deliver up to 7,500 and 10,000 gates by the end of 2026 and in 2027 respectively. Then, in 2028, IBM scientists plan on creating Nighthawk-based systems with up to 1,000 qubits connected using long-range couplers to achieve 15,000 two-qubit gates.

Loon, meanwhile, is a smaller chip with just 112 qubits that IBM scientists say demonstrates all the hardware elements of fault-tolerant quantum computing. These technologies are engineered to address the extremely high failure rate in qubits — a field known as quantum error correction (QEC). QEC is the main reason why quantum processors are getting more sophisticated and not simply larger in terms of qubit count.

In December 2023, for example, IBM scientists built a massive 1,000-qubit chip, named Condor, but its much smaller 127-qubit cousin, Eagle, was deemed the more exciting prospect from a research standpoint, given its error rate was five times lower. The same can be said for Nighthawk compared with Loon.

IBM Quantum CTO Oliver Dial told Live Science that the scientists needed new features in the processors to implement the error correction codes and the couplers they intend to use in the long term. This includes six-way connections, which allow a qubit to be connected with up to six of its neighbors, rather than the four in the latest QPU. They also needed more layers of routing on the surface of the chip, as well as longer couplers, as well as "reset gadgets" that reset the qubit to the ground state from the excited state.

"With Loon, for the first time, we test all these features together on a 112-qubit device," Dial said. "However, for it to function as a fault-tolerant memory, every one of the 112-plus copies of these features on the chip need to work extremely well. While it's the result we're hoping for, realistically, yield may be low at first on this complex of a device. It's intended to let us iron out problems and learn in advance of Kookaburra next year."

Kookaburra will be another proof-of-concept processor, expected in 2026, that IBM representatives say will be the first modular-designed QPU designed to store and process encoded information — combining logic operations with memory.

Reaching quantum advantage and beyond

In addition to launching two new QPUs, IBM has established a quantum advantage tracker. Quantum advantage is when a quantum computer can demonstrate problem-solving beyond the means of a classical supercomputer.

Demonstrating quantum advantage is difficult because classical computers can't easily verify or replicate the problems that are being tackled by quantum systems. The first three challenges launched as part of the tracker are "observable estimations," "variational problems" and "classically verifiable problems."

The company also delivered an update on the fabrication of quantum processors on a 300mm (12 inches) wafer. This new format, a large disc-shaped semiconductor that reflects light in rainbow colors, halves the time needed to build each processor, while also achieving a 10-times increase in the physical complexity of the quantum chips.

To build these wafers, long cylinders of silicon are sliced into thin disks, with engineers using software to design electric circuits. Automated machines then etch these circuits into the surface of the silicon, deposit new metals and treat the wafers, resulting in a rectangular grid of computer chips on the disk. Engineers fabricate multiple wafer types and then complete additional processing steps, before these are layered and connected in a 3D stack, and hooked up to control electronics.

IBM scientists hope to deliver their first fault-tolerant quantum computing chip, called Starling, by 2029, with a monstrous 2,000-qubit Blue Jay chip set to be released by 2033, according to the company's quantum roadmap.

Time crystals could help create quantum computing data storage that lasts minutes, new research shows — a huge improvement on the milliseconds-long duration of existing quantum data storage.

In the new research, scientists ran experiments on how time crystals interact with mechanical waves. Although time crystals are widely considered extremely fragile, the researchers showed that they could couple the time crystal to a mechanical surface wave without it being destroyed.

"This is for me the most interesting part," study co-author Jere Mäkinen, an academy research fellow at Aalto University in Finland, told Live Science. "It is that you can really couple time crystals in a significant way to another system and harness the inherent robustness of time crystals."

The researchers described their findings in a study published Oct. 16 in the journal Nature Communications.

Making waves in time crystal research

Traditional crystalline structures have a regular arrangement of atoms or molecules in space, but time crystals return to a certain state after regular periods of time. This is not the same as a pendulum, for instance, where the swinging frequency merely reflects the frequency of the oscillating downward force as the gravitational pull vies with the changing orientation of the tension. In the case of a time crystal, although in practice some initial prompt into action is required, the periodicity is acquired spontaneously, without anything driving it at that frequency.

Since they were first proposed in 2012, various setups that act as time crystals have been reported. Mäkinen and his collaborators based theirs on quasiparticles called magnons — collective waves in the value of a quantum property known as spin. They created magnons in "superfluid helium-3," helium where the nuclei have two protons and just one neutron so that the spins of the particles in the nucleus cannot cancel out.

They cooled the helium 3 to cryogenic temperatures so that the dynamics of the atoms cause them to effectively attract each other, albeit weakly, and they reorganize into quasiparticles known as Cooper pairs. As Cooper pairs, these quasiparticles are limited to just one available quantum state, which thus eliminates the fluid viscosity.

It turns out that sloshing the superfluid helium 3 to and fro with a mechanical surface wave has an interesting effect on it that boils down to the influence of the surface on the spin and orbital angular momentum of the Cooper pairs, which are the properties used to characterize the superfluid. To picture this, think of the influence of a wall on the possible orbits of a ball spun at the end of a string: in free space, the ball orbitals can take on any orientation in three dimensions, but take it close to a wall and some of these orbitals are no longer possible.

Mäkinen and his collaborators recognized that this would influence the period of the magnon time crystal. In their experiments, they found that the time crystal could survive the interaction for up to a few minutes. This suggests that it may be possible to couple data from quantum computers to the time crystal through a similar interaction for storage.

In quantum computers, each qubit can be in a superposition of two binary states at once, which is the basis for theoretically higher processing power. Memory in quantum computers, therefore, must store data that preserves this indefinite quality of the qubit state.

Memory technologies in today's quantum computers commonly use the orientation of spin to store data, but these spin states are easily upset by environmental disturbances such as thermal noise. These disturbances nudge them into one or the other possible state, meaning the quantum nature of the data being stored is lost. As such, spin quantum memory only lasts a few milliseconds.

In contrast, the magnons that Mäkinen and his collaborators created lasted minutes, even with the disturbance of the mechanical surface wave. Since the surface wave leaves an imprint on the magnon time crystal frequency, it can be used to "write" the quantum data to be stored. With longer quantum memory, more quantum processing operations can be implemented on the data before it deteriorates, allowing for more complex tasks.

Textbook analogies

After looking at the experimental data, the team also found several similarities to optomechanics, where light and mechanical resonators interact. An example is the barely perceptible impact of a photon hitting a mirror attached to a spring, where the spring gains or loses energy as the photon bounces off the mirror.

Drawing parallels between time crystals and optomechanics could reveal theory from the well-established field of optomechanics that can apply to time crystals subject to a mechanical wave, providing a head start in understanding these interactions.

"Optomechanics is such a general theme in many fields of physics, so you can use it in a huge variety of different systems," Mäkinen said.

Nikolay Zheludev, a professor of physics and astronomy at the University of Southampton who also studies time crystals and optomechanics but was not involved in the study, described the study as "interesting.", "It opens a direction of research in the physics of nonequilibrium systems with potential implications for advancing quantum sensing and quantum control," he told Live Science in an email.

Mäkinen said he is keen to explore different types of setups to couple mechanically to the time crystal, such as with a nanofabricated electromechanical resonator, which would have a much lower mass than the superfluid surface wave. "The obvious idea is to really go towards the quantum limit and see how far we can push it," he said.

Scientists at Quantinuum have unveiled the world's most powerful quantum computer. The team claims the new system is capable of solving a problem that a supercomputer could handle only if it consumed more power than the total wattage of a quasar — one of the brightest objects in the universe.

At the heart of the new machine, known as Helios, is a quantum processing unit (QPU) with 98 physical qubits made of barium ions. These qubits are arranged in a "junction ion trap" formation — a small, ring-like structure that forms a crossover junction at the base, before extending into two parallel rods.

This unique arrangement of qubits boosts error detection and correction to render much better performance than existing QPUs when running calculations, the scientists said. They described their findings in a new study published Nov. 5 by the Sandia National Laboratory in partnership with the company.

The scientists claim this is the most powerful quantum computer in the world, after it passed a series of benchmark experiments. They also used the machine to simulate a superconducting metal and made a new discovery about the atomic behavior of the material.

"Currently, this is easily the most powerful quantum computer on Earth," David Hayes, director of computational design and theory at Quantinuum, told Live Science. "I don't feel shy about that at all."

Anatomy of a quantum computer

Scientists meshed the 98 physical qubits into 48 fully error-corrected logical qubits (48 pairs with two spares) — collections of qubits that share data to minimize the chances of failure if an error were to occur in one of them. In doing so, the team achieved "better than break-even performance," Hayes said.

"Better than break-even performance" means the processor performs better in real-world calculations with error-correction codes applied than without any error-correction efforts — something that isn't as easy as it sounds.

So far, scientists have assumed that they would need a 10:1 ratio for logical qubits (approximately 10 physical qubits entangled to create one logical qubit), Hayes said, but Quantinuum scientists got that down to 2:1.

They also ran experiments with 50 and 96 logical qubits, but the results weren't as impressive. Nevertheless, achieving good results with 46 will make it easier to build much larger machines in the future, when scientists scale them up to millions of qubits — which is necessary to outpace the fastest supercomputers, Hayes added.

In addition, the scientists created a new programming language, called Guppy, which is based on the widely used Python language and is designed to be compatible with future fault-tolerant systems. They also built a new control stack from scratch so that the control engine — the classical brain of the machine — could detect and resolve errors in real time.

The control engine works like a classical computer and designs the quantum circuits as they're running. Then, Helios uses Nvidia GPUs to decode error information and then send the corrections back to the quantum computer to reduce errors.

"It now has to think fast enough so that it can plan and change the quantum problem quickly enough so that the qubits aren't sitting around and dephasing and decohering [losing the delicate quantum state in which clacluations can run] and all this stuff," Hayes said. "We've finally mastered this real-time control engine that's necessary for fault tolerance, and it's an integral part of the new machine."

Quantum computing margin of error

"The thinking was, when we first started with [Quantinuum's previous QPUs] H1 and H2, we were just trying to get something running — build a system," Hayes said. "And as soon as we did it, we started looking into these quantum error correction experiments, and started realizing pretty quickly that we needed something else."

In the study, the machine scored much higher in various quantum computing benchmarking tests than any machine publicly unveiled so far. The QPU registered 99.921% fidelity across all qubit pairs and 99.9975% fidelity across single-qubit quantum gates (calculations that run on single qubits), they reported.

The benchmarking experiments included the widely used random circuit sampling (RCS) benchmark that Google first devised in 2019 and then pushed to the limits with its Willow QPU in 2024. Quantinuum broke that record last year with its 56-qubit H2-1 quantum computer.

Although many quantum computers have more physical qubits than the new system, performance depends more on the quality of the qubits — and minimizing their propensity to fail. This is why scientists have recently focused on quantum error correction (QEC).

This aims to address the extremely high error rate in qubits relative to bits in classical computing; 1 in 1 trillion bits fail in conventional computers, versus approximately 1 in 1,000 qubits in quantum computers (without any intervention or error-correction efforts).

Using quantum computers for new discoveries

To test out their new machine, the scientists used Helios to model a high-temperature superconducting metal to discover previously unknown electron behavior. They detailed the findings in another study published Nov. 3 to the arXiv preprint database.

In the study, they found that the electrons pair up through entanglement, such that they have a shared identity while the metal is in a superconducting state. This "signature of superconductivity" is not present when the metal is not superconducting, Hayes said.

The model was based on a previous experiment in which scientists shined a light on a chunk of metal — the recently discovered La₃Ni₂O — to make it superconducting at room temperature for a very short time. The simulation revealed the superconducting signatures. In a "wet lab" where you actually have the chunk of metal present, Hayes said, you can't see this behavior in individual electrons.

Scientists have previously run other experiments on analog quantum simulators — simple quantum systems that emulate more complex ones — that model how the chunk of metal might behave, Hayes noted. However, they can't measure the individual particles and examine them in the same way a digital quantum computer can. He added that the new machine is the first quantum computer capable of observing this phenomenon.

Having revealed the new quantum computing architecture, Hayes is confident that scientists can begin scaling it up so that many of these junction ion traps can work together in future machines.

"You can kind of think of it as a traffic intersection for the qubits to route them really efficiently and pair them up," Hayes said, referencing the junction following the ring in the new arrangement. "And now that we have this one working, we think that it should be pretty straightforward to insert a lot of these things trying to close the window into the next- generation machine and really scale these machines up to huge numbers."

Google scientists have created a new algorithm that can solve problems on a quantum processor 13,000 times faster than the world's fastest supercomputers. They say it brings us one step closer to using quantum computers in drug discovery, materials science and many other scientific applications.

The researchers say the new algorithm, dubbed Quantum Echoes, is a breakthrough because it achieves quantum advantage while being the first such algorithm that can be verified independently by running it on another quantum computer.

The Quantum Echoes algorithm achieved its superfast result in a benchmarking experiment run on Google's Willow quantum processing unit (QPU). The researchers outlined how the algorithm works in a new study published Oct. 22 in the journal Nature.

"Quantum algorithms tell the quantum computer how to solve problems in the most efficient manner, analogous to software developments in classical computing," Xiao Mi, a Google Quantum AI research scientist who oversaw the completion of this work, told Live Science in an email. "Both the software and hardware elements have to exist and work together in order for either classical or quantum computing to help solve problems in the future."

While the scientists demonstrated the new algorithm's quantum advantage in the first study, they also wanted to show that it could be used to address a practical problem. In a second study, published Oct. 22 in the arXiv preprint database, the same team designed a quantum circuit to mimic the dynamics of molecules in a nuclear magnetic resonance (NMR) spectroscopy laboratory.

In doing so, they discovered previously unknown details of the atomic spacing and structures of two molecules with 15 and 28 atoms respectively — [4-¹³C]-toluene and [1-¹³C]-3',5'-dimethylbiphenyl (DMBP).

The system used in this experiment was small (15 qubits), but future work will enable researchers to simulate molecules that are four times larger — a scale that’s impossible for classical simulations, the team said in the study.

Echoes from the past

The new research has built on decades of work that began in the 1980s with research by Michel Devoret, professor of physics at the University of California and Google Quantum AI's chief scientist of quantum hardware. Devoret was the joint winner of the 2025 Nobel Prize in physics for the work and is a co-author of the study.

"Today, we are announcing this breakthrough algorithm that actually marks another milestone in which the computation is done, the quantity of which is verifiable. So if another quantum computer would do the same calculation, the result would be the same. So this marks a new step towards full-scale quantum computations," Devoret said in a press briefing. "This Quantum Echoes algorithm is not only verifiable, so that its result can be obtained by another similar quantum computer, but it presents a quantum advantage; it realizes a computation that would take much longer than with classical hardware."

The Quantum Echoes algorithm works in several stages, amounting to a highly advanced echo in which a signal is sent into the quantum system and then reversed to listen for the "echo" that comes back, all amplified by constructive interference (a phenomenon in which quantum waves compound to become stronger).

First, scientists ran a series of operations, or quantum gates, on an entangled 105-qubit array on the Willow QPU. Next, one qubit was perturbed, or deviated, before they ran the same exact operations in reverse. The result was a curious "butterfly effect" that could be used to reveal information about the quantum system. The scientists then used this algorithm to measure distances between atoms in the two molecules.

To confirm the performance of the algorithm on Willow versus on classical supercomputers, the scientists conducted rigorous "red-teaming" tests, borrowing from cybersecurity methods to verify the robustness of the results. These tests ran for the equivalent of 10 years.

"Certainly, it throws down the gauntlet for any skeptics to try to reproduce their results classically," Scott Aaronson, chair of computer science at The University of Texas at Austin told Live Science. "Compared to previous quantum supremacy demonstrations, the big advantage here is that the output is a single number rather than a sample from a distribution, and therefore is in principle, efficiently verifiable — if not using a classical computer, then at least using a second quantum computer."

Aaronson added that verifiable quantum supremacy is one of the biggest challenges in the field. He noted that Google's goal across both new studies was not to solve a commercially useful problem but to get a clear advantage over a classical computer and enable another quantum computer to verify the answer independently.

Google launched the Willow quantum computing chip in December last year. The new processor demonstrated that as the number of qubits are scaled up, the errors that occur reduce exponentially, marking a key milestone in quantum computing research. But hardware improvements are not enough on their own — even if the machines could be scaled the millions of qubits required to beat classical computing. That's because the software and hardware components need to work together to find the most efficient route to solving a problem, as Mi noted.

Google scientists claim that we will begin to see practical applications that are only possible with quantum computers in as little as five years. However, we would still need to scale up the hardware so that machines can operate with millions of qubits — something which is difficult to imagine today because the most powerful quantum computers only have 100s or 1,000s of qubits.

Researchers have developed an experimental method for determining whether the functions performed by a quantum computer are the result of quantum mechanics — or just a clever twist on classical physics.

In a landmark study published April 22, 2025, in the journal Physical Review X, the researchers describe an experimental test that demonstrates and certifies computing activity that can only be achieved through quantum mechanics.

This "quantum lie detector," as the researchers described it, was created by reframing a famous test for quantum mechanics and engineering a purpose-built quantum computer trained to operate in ways that would be fundamentally impossible to achieve on a classical system.

The scientists accomplished this by creating a programmable, 73-qubit "honey-comb" quantum processor and training it using a hybrid quantum-classical technique called a Variational Quantum Circuit (VQC). This is a machine learning loop where a classical computer iteratively helps a quantum computer perform a task with greater accuracy.

In this case, the computer’s task was to reach an energy state so low that it couldn’t be achieved via classical physics. By confirming this energy state, the researchers demonstrated quantum mechanics.

Tapping into the laws of quantum mechanics

One of the ultimate goals of quantum computing is to push the limits of what computers can do beyond what the laws of classical physics will allow. Binary computers, such as our phones, laptops, PCs, servers and supercomputers are constrained by the fundamental laws of classical physics.

Bits in classical computing use 1s and 0s to conduct complex computations, but they can only process calculations in sequence. Ultimately, there is a limit to what they can accomplish within a feasible amount of time.

Quantum computers, on the other hand, use qubits — the quantum equivalent of a classical bit — to tap into the weird laws of quantum mechanics, such as quantum entanglement, to perform complex computations in parallel. Where a bit’s state can be represented as either on or off (with a 1 or 0), a qubit occupies a superposition of both the on and off states (meaning it could be either state and any combination of states) until it’s measured.

Quantum entanglement occurs when two qubits become correlated over distance. Measuring the state of one reveals the states of any associated entangled qubits. Under the laws of classical physics, this would be akin to flipping a coin in London to determine the results of a simultaneous flip in New York. As more entangled qubits are added to a system, the computational space grows exponentially.

At sufficient size, the theoretical computation space for a quantum computer becomes mathematically intractable for a binary computer system — this is described as "quantum advantage" or "quantum supremacy."

While quantum phenomena can be demonstrated using experiments such as the Double-Slit Experiment, certifying that a multi-qubit system is truly tapping into quantum mechanics is a challenge. It also becomes exponentially more difficult as the number of qubits in a quantum system increases.

The Bell test and spooky action at a distance

Physicists such as Albert Einstein have long contemplated the threshold at which quantum phenomena break the laws of Newtonian physics. Essentially, the problem boils down to whether there is no classical explanation for a quantum operation, or whether we just haven’t found one.

When presented with entanglement, for example, Einstein famously called it "spooky action at a distance." His worldview, based on local realism, insisted that objects are only affected by their immediate surroundings (locality) and that their properties exist definitively before we measure them (realism).

Entanglement breaks this relativity. When two particles become entangled, they exist in a state of nonlocality. To prove this, scientists perform a Bell test, named for Irish physicist John Stewart Bell. This involves measuring entangled particles in multiple, randomly chosen ways and checking the statistical outcomes.

If the correlations between the measured outcomes are stronger than any classical theory could ever allow — a limit known as Bell's Inequality — then the system is said to be nonlocal.

This proves the "spooky action at a distance" is real and not just the result of chance, mathematical trickery or classical simulation.

Brute-force simulations

One of the main hurdles in determining whether quantum computations are actually quantum in nature is the fact that classical computers can simulate quantum states, to a certain point, using brute-force mathematics. This makes it hard to determine exactly what has been going on "under the hood."

Since no red flag or siren indicates that the laws of physics have been broken when a quantum operation is performed, scientists have to find ways to demonstrate the underlying quantum mechanics behind them.

To achieve this, the researchers ran an experiment using a 73-qubit quantum computer by setting it to its lowest possible energy state and then measuring the energy in the system.

In classical physics, the lowest ground state that can be achieved is zero. A ball rolling down a hill has a high, excited energy state. At its lowest energy state, its ground state, the ball is at rest with no energy.

The same ball, operating under the laws of quantum mechanics, however, could have an energy state lower than zero. This is possible through entanglement. If one ball is entangled with another ball, and both are correlated through functionally diametric energy states, one or both can be placed in a negative energy state.

Because this isn’t possible under the laws of classical physics, confirmation of this negative state is, by definition, a certification that the physics driving the system is indeed quantum.

The confirmed result was an energy so low that it fell below the absolute minimum energy level a classical system could ever possess to 48 standard deviations.

The researchers certified these nonlocal correlations in groups of up to 24 qubits within the larger system, the most ever certified at once in this manner, the scientists wrote in the study.

This work establishes a pioneering method for verifying quantum activity, they added.

With further development, these techniques could help engineers certify performance in various quantum architectures, understand when quantum states "decohere" into classical ones and provide the foundation for building even larger, more powerful quantum computers.

Researchers have discovered a way to speed up quantum error correction (QEC) by a factor of up to 100 — a leap that could significantly shorten the time it takes quantum computers to solve complex problems.

The technique, called algorithmic fault tolerance (AFT), restructures quantum algorithms so they can detect and correct errors on the fly, rather than pausing to run checks at fixed intervals.

In simulations, AFT reduced the time and computational effort spent on error correction by up to 100 times while still maintaining accuracy, according to scientists at QuEra. The results, published Sept. 24 in the journal Nature, were based on tests run on a simulated neutral-atom quantum computer.

In an email to Live Science, Yuval Boger, chief commercial officer at QuEra, said the results marked "a major milestone on the roadmap to practical, large-scale quantum computers," with hardware tests likely to happen "in the next year or two."

"Practical fault-tolerant quantum computing requires both scalable hardware and efficient error correction. AFT directly addresses the efficiency side by removing a major bottleneck," Boger said. "While we’re not at full fault-tolerant systems yet, this result moves the timeline forward significantly, showing that the enormous overhead once assumed is not inevitable."

What is fault-tolerant quantum computing?

Quantum computers can theoretically process information faster than even today’s most powerful supercomputers, which themselves are orders of magnitude more powerful than a top-end PC.

The issue is that qubits, the quantum equivalent of classical computer bits, are notoriously fragile. To perform a reliable calculation, qubits must maintain a delicate quantum state, known as "coherence," long enough to process information. Even the smallest environmental disturbance — be it heat, noise, or electrical interference — can disrupt this state. When this happens, any information held by a qubit is destroyed.

Fault-tolerant quantum computing allows quantum systems to run longer, more complex calculations without being derailed by interference. It typically relies on QEC technologies like logical qubits, which protect information by sharing the same data across many physical qubits — often atoms, ions or superconducting circuits.

Since directly measuring a qubit directly destroys its quantum state, QEC ensures errors can be detected and corrected without collapsing the encoded information. However, it also adds a lot of computational overhead because it involves inserting error checks at regular intervals.

AFT works differently, instead restructuring quantum algorithms so that error detection is built into the flow of the computation itself.

"Instead of needing dozens of repetitions per operation, only a single check per logical step may be enough," Boger told Live Science. "This is a breakthrough because it dramatically reduces the overhead of error correction, meaning quantum computers can perform useful calculations with far less hardware and much faster execution times."

Why AFT and neutral-atom systems work together

Neutral-atom quantum computers may be particularly well-suited for AFT, QuEra representatives said in a statement. These store quantum information in individual atoms that are held in place and controlled by finely tuned laser beams, providing a built-in flexibility that enables qubits to be repositioned as needed.

"In these systems, any atom can be moved to interact with any other, which means they aren’t limited by fixed wiring like superconducting qubits are. This "all-to-all" flexibility is a natural fit for fault-tolerant schemes," Boger said. He added that they support parallel operations, meaning you can give the same instructions to multiple qubits at once. If one of them makes a mistake, the error is isolated and doesn't spread throughout the rest of the system.

Neutral-atom machines also operate at room temperature, avoiding the complexity and expense of extreme cryogenic cooling. "Taken together — flexibility, simultaneous operations and simpler infrastructure — neutral atoms are uniquely positioned to take advantage of algorithmic fault tolerance, even though other platforms may benefit as well," said Boger.

When the researchers applied AFT to simulations of QuEra’s neutral-atom architecture, they found it cut the time and computational resources needed for error correction by between 10 and 100 times, depending on the algorithm.

This kind of acceleration could make quantum computers fast enough to solve real-world problems that were previously considered out of reach, Boger said.

"Imagine an algorithm to optimize the global routes of shipping containers. Such an optimization algorithm might require a month of runtime on a future error-corrected quantum computer. By the time the algorithm finishes, conditions have changed and thus the results are no longer useful. With this new method, the same calculation could potentially be finished in less than a day, moving it from theoretical to practical usefulness."

Scientists at Caltech have conducted a record-breaking experiment in which they synchronized 6,100 atoms in a quantum array. This research could lead to more robust, fault-tolerant quantum computers.

In the experiment, they used paired neutral atoms as the quantum bits (qubits) in a system and held them in a state of “superposition” to conduct quantum computations. To achieve this, the scientists split a laser beam into 12,000 "laser tweezers" which together held the 6,100 qubits.

As described in a new study published Sept. 24 in the journal Nature, the scientists not only set a new record for the number of atomic qubits placed in a single array — they also extended the length of "superposition" coherency. This is the amount of time an atom is available for computations or error-checking in a quantum computer — and they boosted that duration from just a few seconds to 12.6.

The study represents a significant step towards large-scale quantum computers capable of technological feats well beyond those of today’s fastest supercomputers, the scientists said in the study. They added that this research represents a key milestone in developing quantum computers that use neutral-atom architecture.

This type of qubit is advantageous because it can operate at room temperature. The most common type of qubits, made from superconducting metals, needs expensive and cumbersome equipment to cool the system down to temperatures close to absolute zero.

The road to quantum advantage

It’s widely believed that the development of useful quantum computers will demand systems with millions of qubits. This is because each functional qubit needs several error-corrected qubits to provide fault tolerance.

Qubits are inherently "noisy," and tend to decohere easily when faced with external factors. As data is transferred through a quantum circuit, this decoherence distorts it, making the data potentially unusable. To counteract this noise, scientists must develop fault-tolerance techniques in tandem with methods for qubit expansion. It's the reason a huge amount of research has so far gone into quantum error correction (QEC).

Many of today’s systems are considered functional, but most wouldn’t meet a minimum threshold for usefulness relative to a supercomputer. Quantum computers built by IBM, Google and Microsoft, for example, have successfully outperformed classical computers and demonstrated what’s often referred to as "quantum advantage."

But this advantage has been largely limited to bespoke computational problems designed to showcase the capabilities of a specific architecture — not practical problems. Scientists hope that quantum computers will become more useful as they scale in size and as the errors that occur in qubits are managed better.

"This is an exciting moment for neutral-atom quantum computing," said lead author Manuel Endres, professor of physics at Caltech and principal investigator on the research, in a statement. "We can now see a pathway to large error-corrected quantum computers. The building blocks are in place."

More notable than the sheer size of the qubit array are the techniques used to make the system scalable, the researchers said in the study. They fine-tuned previous efforts to make approximately 10-fold improvements in key areas such as coherence, superposition and the size of the array. Compared to previous efforts, they scaled from hundreds of qubits in a single array to more than 6,000 while maintaining 99.98% accuracy.

They also showed off a new technique for "shuttling" the array by moving the atoms hundreds of micrometers across the array without losing superposition. It’s possible that, with further development, the use of shuttling could provide a new dimension of instant error-correction, they said.

The team's next steps involve linking the atoms together within the array through a state of quantum mechanics called entanglement, which would lead to full quantum computations. Scientists hope to exploit entanglement to develop stronger fault-tolerance methods with even more accurate error-correction, they added. These techniques could prove crucial to achieving the next milestone on the road to useful, fault-tolerant quantum computers.

A U.K. startup has created the world's first silicon-based quantum computer manufactured using the same transistor technology found in nearly all modern digital electronics.

The machine is built using the complementary metal-oxide-semiconductor (CMOS) chip fabrication process — the same used to create the chips for devices like smartphones, laptops and digital cameras.

CMOS technology is so widely used because it produces chips that don't draw power when idle. Its integration in a quantum computer paves the way for broad adoption and less expensive manufacturing processes.

Another important element of the machine, built by the company Quantum Motion, is its relatively small footprint. The machine can be housed in just three 19-inch server racks, including the dilution refrigerator and integrated control electronics that manipulate the qubits and produce the extremely low temperatures required to maintain their fragile quantum states.

The system combines a quantum processing unit (QPU) with a user interface and industry-standard control software — the specialized layer that acts as the interpreter between a high-level quantum program (the algorithm) and the physical quantum hardware (the qubits), such as Qiskit and Cirq — to provide a complete quantum computing platform. It uses spin qubits — a type of qubit that encodes quantum information in the spin (intrinsic angular momentum) of an elementary particle, most commonly a single electron.

It's also highly scalable, Quantum Motion representatives said Sept. 15 in a statement. The QPU itself is based on tile architecture — a modular design approach where a processor or a system-on-a-chip (SoC) is built from smaller, self-contained and specialized units called tiles or chiplets.

The QPU condenses the necessary compute, readout and control elements into a single, dense array that can be deployed repeatedly on a single chip. This means that future iterations of the QPU, the physical hardware where quantum computation happens, can be upgraded to include millions of qubits, representatives said, and the system could allow future versions of the company's QPU to be easily swapped in for the existing processor.

“This is quantum computing’s silicon moment,” said James Palles‑Dimmock, CEO of Quantum Motion. “Today’s announcement demonstrates you can build a robust, functional quantum computer using the world’s most scalable technology, with the ability to be mass-produced.”

Related: Physicists force atoms into state of quantum 'hyper-entanglement' using tweezers made of laser light

Quantum Motion representatives say that this system is the first step to delivering commercially viable quantum computers within the decade.

The system is currently deployed at the U.K. National Quantum Computing Centre (NQCC) – a national lab for quantum computing, funded primarily through the UK Research and Innovation (UKRI) program. UKRI is a public body that directs research and innovation funding in the U.K.

Quantum Motion's system also represents the first silicon spin‑qubit computer developed under the auspices of NQCC’s Quantum Computing Testbed Programme, an initiative to build seven prototype quantum computers using differing technologies and test their viability.

The computer builds on research undertaken by Quantum Motion in conjunction with University College London (UCL) to create more fault-tolerant quantum systems. That research demonstrated 98% accuracy in two-qubit gates, the fundamental building block of a quantum circuit. That's a world-leading mark in qubits fabricated in natural silicon on a 300mm wafer scale, the same material used in the new computer.

Fault tolerance is critical to quantum computing because qubits are notoriously fragile and error-prone. The instability is due to a property called decoherence.

Superposition (the ability for a qubit to exist in multiple states at once) and entanglement (the ability of two or more qubits to be connected to one another and share the same state across any distance, so that altering one alters the other simultaneously), the keys to quantum computation, are both fragile states that can be destroyed by even the slightest interaction with the environment.

Changes in temperature, electromagnetic interference or other environmental factors can distort or collapse those properties, leading to inaccurate results. That fragility is one of the biggest obstacles to scalable and powerful quantum computing. That's why plenty of quantum computing research is in the area of quantum error correction (QEC).

As part of the SiQEC silicon quantum error correction project, Quantum Motion leverages silicon spin qubits created using standard 300 mm semiconductor manufacturing processes and its error correction research to build fault-tolerant architectures that could scale to the millions of qubits needed for quantum advantage.

The primary edge this kind of manufacturing holds over other processes is the commonality of the silicon manufacturing. Because the facilities, standards and techniques for effectively mass-producing these kinds of chips are already well-established, they can be produced more cheaply, quickly and at a greater scale than other, more specialized components.

Protein-based quantum bits (qubits) could be the key to accelerating biological research at the smallest of scales, thanks to a new scientific breakthrough.

Researchers from the University of Chicago have discovered a way to turn a fluorescent protein into a biological qubit that can be built directly inside a cell, then used as a way to detect magnetic and electrical signals within the cell. This breakthrough was detailed in a paper published Aug. 20 in the journal Nature.

"Our findings not only enable new ways for quantum sensing inside living systems but also introduce a radically different approach to designing quantum materials,” said Peter Maurer, co-principal investigator and assistant professor of molecular engineering at UChicago, in a statement. “Specifically, we can now start using nature’s own tools of evolution and self-assembly to overcome some of the roadblocks faced by current spin-based quantum technology.”

By developing biological qubits that can be deployed inside cells using existing proteins already employed in microscopy, this research bypasses the need to retrofit existing quantum devices to work in biological systems. This could eventually lead to quantum sensors that don’t need the extreme cooling and isolation normally needed for quantum technology.

Fluorescent findings

Fluorescent proteins, which can be found in a variety of marine organisms, absorb light at one wavelength and emit it at another, longer wavelength; this is, for instance, what gives some jellyfish the ability to glow. As such, they are used by biologists to tag cells through the genetic encoding and in the fusing of proteins.

The researchers found that the fluorophore in these proteins, which enables the immittance of light, can be used as qubits due to their ability to have a metastable triplet state. This is where a molecule absorbs light and transitions into an excited state with two of its highest-energy electrons in a parallel spin. This lasts for a brief period before decaying. In quantum mechanical terms, the molecule is in a superposition of multiple states at once until directly observed or disrupted by an external interference.

Related: Tiny cryogenic device cuts quantum computer heat emissions by 10,000 times — and it could be launched in 2026

To harness this, the researchers developed a custom confocal microscope — a optical system, comprising a series of lenses and mirrors, that uses laser light to produce high-resolution images of biological samples — to optically address the spin state of enhanced yellow fluorescent protein (EYFP) and use it as a qubit in purified protein, a human kidney cell and E.coli bacteria.

The laser microscope initially used a 488-nanometer optical pulse to induce a spin state in the EYFP. A near-infrared laser pulse then triggered a readout of the triplet spin state with "up to 20% spin contrast" — meaning the researchers could see enough differences in spin states to use the protein as a working qubit.

Once the spin has been initialized, the researchers used microwaves to keep the spin in a coherent oscillation between two levels — thus the protein behaved as a qubit for around 16 microseconds before the triplet state decayed.

Biological breakthrough

Observing how the electrons pulse from being hit by a laser means the biological qubit can be used as a quantum sensor, picking up what’s happening inside a cell.

This could yield insight into biological functions at the nanoscale, such as protein folding, the tracking of biochemical reactions in cells and monitoring how drugs bind to target cells and proteins, the scientists said in the study. It could also lead to advancements in medical imaging and the early detection of disease pathways.

While the biological qubit could shake up biological sensing and open up new ways to create quantum sensors, there are hurdles still to overcome.

To effectively manipulate the spin state of the fluorescent protein, it needed to be kept at liquid-nitrogen temperatures. And while the biological qubit proved it could be used effectively in the complex environment of a mammalian cell — a significant part of the breakthrough — it still needed to be cooled to a temperature of 175 kelvin (–98.15 degrees Celsius). At room temperature, this technique still functions in bacterial cells, with the researchers optically detecting magnetic resonance, but only with up to 8% contrast, and with a rapid depletion of the EYFP spin state.

The sensitivity of the biological quantum sensors also lags behind solid-state sensors, such as those made from defects in diamond. So there’s still work to be done on stability and sensitivity before biological qubits and quantum sensors in cells can become practical tools for use in biology and medicine.

But this is a breakthrough that's gone beyond the proof-of-concept stage, and being encoding a qubit directly into a cell opens up a new avenue for quantum technology, where the boundaries between quantum physics and biology are blurred.

Scientists have sent quantum signals over standard fiber-optic cables using the same connectivity that powers today’s web, in what could be a major step towards a working quantum internet.

In a study published Aug. 28 in the journal Science, researchers used a custom-built quantum chip to package quantum data alongside a standard optical signal and transmit them over commercial infrastructure.

The breakthrough marks the first time quantum data has been sent using Internet Protocol (IP), the same communications standard that underpins today's broadband networks. The results suggest that quantum communications could run on networks already in use, rather than needing dedicated infrastructure.

"Unlike earlier experiments that required isolated, lab-based setups or specialized infrastructure, this approach integrates quantum communication into real-world networks for the first time," senior study author Liang Feng, professor of materials science and electrical engineering at the University of Pennsylvania, told Live Science in an email.

"Our Q‑Chip enables control of quantum signals and classical signals, so they travel together over the same fiber‑optic cables, using standard internet protocols."

Why can't the internet send quantum data?

Quantum data is carried by qubits — the basic units of quantum information. Unlike classical computer bits, which are represented as either 0 or 1, qubits can exist in a superposition of both states.

Related: Scientists use quantum machine learning to create semiconductors for the first time – and it could transform how chips are made

Qubits can also become entangled, meaning the state of one is symbiotically linked to the state of another, no matter how far apart they are. These properties enable quantum computers to perform calculations far beyond the reach of conventional computers — in parallel rather than in sequence.

However, these same properties also make quantum data notoriously fragile. Quantum states collapse when they're observed or measured, making quantum information extremely difficult to work with. In classical internet, traffic is directed by routers that read and interpret information as it moves through the network. This can't be done with quantum particles without destroying the very data being transmitted,because the superposition collapses as soon as it is observed.

How the Q-Chip works

The Q‑Chip, which stands for "quantum-classical hybrid internet by photonics", tackles this challenge by pairing each quantum signal with a classical "header" — a data packet containing routing and timing information that’s encoded into a fiber-optic laser pulse.

As this information travels through the network, it’s inspected by routers — devices that direct internet traffic by reading packet information and forwarding it to the correct destination. Routers use the header to determine where the data should go and how to get it there.

By timing the classical and quantum signals to travel together in a synchronized pulse, the chip enables routers to read the header's navigation information without interacting with or disrupting the quantum signal. This enables both to travel together via standard IP protocols.

While researchers have previously demonstrated that quantum data can be transmitted over standard fiber-optic cables, including alongside classical data in the same wavelength band, this latest study marks the first time that quantum signals have been transmitted using standard IP on live, real-world infrastructure.

This is crucial because it avoids the need for a separate quantum-only network, significantly lowering the barrier to deploying and scaling a quantum internet, said Feng.

"Using standard IP protocols means the Q‑Chip allows quantum communication to be managed like regular internet traffic with the already-developed tools for routing, addressing and coordination," he told Live Science.

"By attaching classical 'headers'" to quantum data, the Q‑Chip can route and manage quantum signals using the developed classical photonic devices, systems and infrastructure without disturbing the delicate quantum states, making this the first practical demonstration of quantum communication that fits within existing internet architecture."

To test the system, the team built a simple connection between a server and a receiver node, using a 1-kilometer (0.6 miles) stretch of commercial fiber operated by telecommunications company Verizon.

Because the classical header and quantum signal respond to interference from the environment in similar ways, the team could use the classical signal to correct for noise without disturbing the quantum state. This ensured the data reached its destination intact.

While the pilot setup was small, the researchers believe it marks a foundational step toward a full-scale quantum internet that could link quantum devices — particularly as the Q-Chip is made of silicon and fabricated using existing processes, meaning it can be mass-produced.

"In the next 5-10 years, the early stages of a quantum internet will likely focus on local networks and/or metro-scale quantum internet," Feng told Live Science. "Applications [could include] secure communication, interconnecting quantum computers and distributed quantum sensing such as ultra-precise navigation or timing."

Researchers have developed a tiny device that extinguishes one of the biggest heat sources in quantum computers, cutting their running costs and potentially bringing these machines closer to commercial reality.

Most quantum computers operate at temperatures close to absolute zero (459.67 degrees Fahrenheit, or minus 273.15 degrees Celsius) using specialized cooling equipment to maintain the delicate quantum states of qubits — the core processing units of quantum systems.

Cryogenic amplifiers are also used in quantum computers to boost the extremely weak signals qubits emit at these ultra-low temperatures. This makes it possible to accurately measure their quantum states — which is needed in order to understand what the quantum computer is actually doing.

The challenge with existing amplifiers used to measure qubit behaviour — or any electronics used in quantum computers, for that matter — is that they generate heat. This means the quantum systems require additional cooling systems that add bulk and cost, both of which present major barriers to making quantum systems practical and scalable.

Now, Qubic, a Canadian startup, has devised a cryogenic traveling-wave parametric amplifier (TWPA) made from unspecified "quantum materials" that enables an amplifier to operate with virtually zero heat loss, representatives from the company said in a statement.

They added that this device reduced thermal output by a factor of 10,000 — down to practically zero.

Related: Why quantum computing at 1 degree above absolute zero is such a big deal

The company plans to bring its amplifier to market in 2026.

"The quantum computing industry continues to progress quickly, yet technological barriers remain, and these must be overcome before the industry can deliver utility-scale quantum computers," Jérôme Bourassa, CEO and co-founder of Qubic Technologies, said in the statement. "This project will produce a new type of amplifier which will remove one of those key barriers."

There's been a huge amount of research into how quantum computers can break through the practicality barrier. Scientists have also been exploring quantum error correction (QEC) s to reduce the error rates in qubits and make them more usable.

While some teams have focused on cooling system innovations — from autonomous quantum fridges to cryogenic control chips — other work has used photonic, or light-based, qubits that can operate at room temperature and don't need complex cooling systems.

Then there are more radical approaches like ETH Zürich's, which developed a fully mechanical qubit that eschews conventional quantum system design entirely.

Japan has switched on the first quantum computer that has been designed and built with components from the country. The system is now ready to take on workloads from its base at the University of Osaka’s Center for Quantum Information and Quantum Biology (QIQB).

The new system, which went live on July 28, replaces all previously imported components with homegrown technologies, University of Osaka representatives said in a statement. It will also run on open-source software developed in Japan, called the Open Quantum Toolchain for Operators and Users (OQTOPUS).

The system uses a quantum chip with superconducting qubits — quantum bits derived from metals that exhibit zero electrical resistance when cooled to temperatures close to absolute zero (minus 459.67 degrees Fahrenheit, or minus 273.15 degrees Celsius). The quantum processing unit (QPU) was developed at the Japanese research institute RIKEN.

Other components that make up the "chandelier" — the main body of the quantum computer — include the chip package, delivered by Seiken, the magnetic shield, infrared filters, bandpass filters, a low-noise amplifier and various cables.

These are all housed in a dilution refrigerator (a specialized cryogenic device that cools the quantum computing components) to allow for those extremely low temperatures. It also comes alongside a pulse tube refrigerator (which again cools various components in use), controllers and a low-noise power source.

OQTOPUS, meanwhile, is a collection of open-source tools that include everything required to run quantum programs. It includes the core engine and cloud module, as well as graphical user interface (GUI) elements, and is designed to be built on top of a QPU and quantum control hardware.

Related: Scientists hit quantum computer error rate of 0.000015% — a world record achievement that could lead to smaller and faster machines

A new frontier of computing

Quantum computing has the potential to outpace the world's fastest supercomputers and solve problems by making calculations and running simulations far beyond what technology is capable of today. Scientists speculate that quantum computers could be useful in drug discovery, easing traffic flows through a city, and finding the best delivery routes for a logistics company, among plenty of other endeavors.

This is because it can process calculations in parallel, rather than in sequence, by tapping into the weird laws of quantum mechanics. The idea is that the more qubits added to a system, the more powerful the system becomes.

However, there are plenty of barriers to simply adding qubits to quantum computers — in particular, scientists are trying to solve the extremely high error rate that occurs during calculations. For this reason, most research is currently centered on quantum error correction (QEC).

Japan's first quantum computer was showcased at Expo 2025, held in Osaka from Aug. 14 to Aug. 20..At the exhibition, organizers showcased key components in the quantum computer. Visitors could connect to the system remotely through the cloud and run basic quantum programs. The exhibit also included interactive elements enabling visitors to explore quantum entanglement and other quantum phenomena.

A US military space-plane, the X-37B orbital test vehicle, is due to embark on its eighth flight into space on August 21, 2025. Much of what the X-37B does in space is secret. But it serves partly as a platform for cutting-edge experiments.

One of these experiments is a potential alternative to GPS that makes use of quantum science as a tool for navigation: a quantum inertial sensor.

Satellite-based systems like GPS are ubiquitous in our daily lives, from smartphone maps to aviation and logistics. But GPS isn't available everywhere. This technology could revolutionize how spacecraft, airplanes, ships and submarines navigate in environments where GPS is unavailable or compromised.

In space, especially beyond Earth's orbit, GPS signals become unreliable or simply vanish. The same applies underwater, where submarines cannot access GPS at all. And even on Earth, GPS signals can be jammed (blocked), spoofed (making a GPS receiver think it is in a different location) or disabled — for instance, during a conflict.

This makes navigation without GPS a critical challenge. In such scenarios, having navigation systems that function independently of any external signals becomes essential.

Traditional inertial navigation systems (INS), which use accelerometers and gyroscopes to measure a vehicle's acceleration and rotation, do provide independent navigation, as they can estimate position by tracking how the vehicle moves over time. Think of sitting in a car with your eyes closed: you can still feel turns, stops and accelerations, which your brain integrates to guess where you are over time.

Related: 10 things we know about the secret X-37B space plane

Eventually though, without visual cues, small errors will accumulate and you will entirely lose your positioning. The same goes with classical inertial navigation systems: as small measurement errors accumulate, they gradually drift off course, and need corrections from GPS or other external signals.

Where quantum helps

If you think of quantum physics, what may come to your mind is a strange world where particles behave like waves and Schrödinger's cat is both dead and alive. These thought experiments genuinely describe how tiny particles like atoms behave.

At very low temperatures, atoms obey the rules of quantum mechanics: they behave like waves and can exist in multiple states simultaneously — two properties that lie at the heart of quantum inertial sensors.

The quantum inertial sensor aboard the X‑37B uses a technique called atom interferometry, where atoms are cooled to the temperature of near absolute zero, so they behave like waves. Using fine-tuned lasers, each atom is split into what's called a superposition state, similar to Schrödinger's cat, so that it simultaneously travels along two paths, which are then recombined.

Since the atom behaves like a wave in quantum mechanics, these two paths interfere with each other, creating a pattern similar to overlapping ripples on water. Encoded in this pattern is detailed information about how the atom's environment has affected its journey. In particular, the tiniest shifts in motion, like sensor rotations or accelerations, leave detectable marks on these atomic "waves".

Compared to classical inertial navigation systems, quantum sensors offer orders of magnitude greater sensitivity. Because atoms are identical and do not change, unlike mechanical components or electronics, they are far less prone to drift or bias. The result is long duration and high accuracy navigation without the need for external references.

The upcoming X‑37B mission will be the first time this level of quantum inertial navigation is tested in space. Previous missions, such as NASA's Cold Atom Laboratory and German Space Agency's MAIUS-1, have flown atom interferometers in orbit or suborbital flights and successfully demonstrated the physics behind atom interferometry in space, though not specifically for navigation purposes.

By contrast, the X‑37B experiment is designed as a compact, high-performance, resilient inertial navigation unit for real world, long-duration missions. It moves atom interferometry out of the realms of pure science and into a practical application for aerospace. This is a big leap.

This has important implications for both military and civilian spaceflight. For the US Space Force, it represents a step towards greater operational resilience, particularly in scenarios where GPS might be denied. For future space exploration, such as to the Moon, Mars or even deep space, where autonomy is key, a quantum navigation system could serve not only as a reliable backup but even as a primary system when signals from Earth are unavailable.

Quantum navigation is just one part of the current, broader wave of quantum technologies moving from lab research into real-world applications. While quantum computing and quantum communication often steal headlines, systems like quantum clocks and quantum sensors are likely to be the first to see widespread use.

Countries including the US, China and the UK are investing heavily in quantum inertial sensing, with recent airborne and submarine tests showing strong promise. In 2024, Boeing and AOSense conducted the world's first in-flight quantum inertial navigation test aboard a crewed aircraft.

This demonstrated continuous GPS-free navigation for approximately four hours. That same year, the UK conducted its first publicly acknowledged quantum navigation flight test on a commercial aircraft.

This summer, the X‑37B mission will bring these advances into space. Because of its military nature, the test could remain quiet and unpublicized. But if it succeeds, it could be remembered as the moment space navigation took a quantum leap forward.

This edited article is republished from The Conversation under a Creative Commons license. Read the original article.

Mathematicians have found a way to transform an unproductive quantum computing approach by reviving a class of previously discarded particles.

Quantum computers can solve problems beyond the capabilities of classical computers by using principles like superposition. This means a quantum bit, or qubit, can represent both 0 and 1 simultaneously, similar to the famous thought experiment of a cat being both dead and alive. But qubits are extremely fragile. Interactions with the environment can easily disrupt their quantum states. Their fragility makes it difficult to build stable quantum computers.

Now, in a new study published in the journal Nature Communications, mathematicians have shown that when paired with mathematical elements previously thrown out as irrelevant, a kind of quasiparticle called an Ising anyon could help to overcome that fragility. They named the revived components "neglectons."

Ising anyons exist only in two-dimensional systems. They are at the heart of topological quantum computing. It means that anyons store information not in the particles themselves, but in how they loop or braid around one another. That braiding can encode and process information in ways that are far more resistant to environmental noise.

But there's been a major limitation. "The only problem with Ising anyons is that they are not universal,” Aaron Lauda, a professor of physics and mathematics at the University of Southern California, told Live Science. "It’s like when you have a keyboard and it only has half the keys."

Related: Scientists make 'magic state' breakthrough after 20 years — without it, quantum computers can never be truly useful

That's where the overlooked math comes in. The team revisited a class of theories called "non-semisimple topological quantum field theory," is used to study symmetry in mathematical objects.

"This is a key idea in particle physics," Lauda said. "You're able to predict new particles that people didn't know about just by understanding the symmetry of what happens."

In this theory, each particle has a quantum dimension — a number that reflects how much "weight," or influence, it has in the system. If the number is zero, the particle is usually discarded.

"The key idea of these new non-semisimple versions is that you keep those particles, which originally had zero weight," Lauda told Live Science. "And you come up with a new way of measuring the weight. There are some properties that it has to satisfy, and figure out how to make that number not be zero."

The neglected pieces, reinterpreted as particles, filled in the missing capabilities of Ising anyons. The team showed that with just one neglecton added to the system, the particle becomes capable of universal computation just through braiding.

Why do Ising anyons matter?

To see why anyons matter at all, it helps to understand their peculiar behavior in two dimensions.

In three dimensions, particles like bosons and fermions can loop around each other. But those loops can be undone, like slipping a string over or under another. In two dimensions, by contrast, there's no "over" or "under." That means when anyons move around one another, the paths can't be untangled, giving rise to fundamentally new physics.

"The way to think about it," Lauda explained, "is if I start with a state zero and I wrap it around, does it stay in a state zero or some multiple of that? Or does it create a zero and a one? Am I able to mix them and create these superpositions that I need to do quantum computation?"

The key with Ising anyons is to be able to create superpositions. Because these operations depend on the overall shape of the braiding path, rather than on precise locations, they're naturally shielded from many kinds of noise.

The finding doesn't mean we'll have topological quantum computers tomorrow. But it suggests that rather than inventing entirely new materials or exotic particles, researchers may just need to look at familiar systems through a new mathematical lens.

Researchers at IBM and Moderna have successfully used a quantum simulation algorithm to predict the complex secondary protein structure of a 60-nucleotide-long mRNA sequence, the longest ever simulated on a quantum computer.

Messenger ribonucleic acid (mRNA) is a molecule that carries genetic information from DNA to ribosomes. It directs protein synthesis in cells and is used to create effective vaccines capable of instigating specific immune responses.

It’s widely believed that all the information required for a protein to adopt the correct three-dimensional conformation is provided by its amino acid sequence or "folding."

Although it’s made up of only a single strand of amino acids, mRNA has a secondary protein structure consisting of a series of folds that provide a given molecule’s specific 3D shape. The number of possible folding permutations increases exponentially with each added nucleotide. This makes the challenge of predicting what shape a mRNA molecule will take intractable at higher scales.

The IBM and Moderna experiment, outlined in a study first published for the 2024 IEEE International Conference on Quantum Computing and Engineering, demonstrated how quantum computing can be used to augment the traditional methods for making such predictions. Traditionally, these predictions typically relied on binary, classical computers and artificial intelligence (AI) models such as Google DeepMind’s AlphaFold.

Related: DeepMind's AI program AlphaFold3 can predict the structure of every protein in the universe — and show how they function

According to a new study published May 9 on the preprint arXiv database, algorithms capable of running on these classical architectures can process mRNA sequences with "hundreds or thousands of nucleotides," but only by excluding higher complexity features such as "pseudoknots."

Pseudoknots are complicated twists and shapes in a molecule’s secondary structure that are capable of engaging in more complex internal interactions than ordinary folds. Through their exclusion, the potential accuracy of any protein-folding prediction model is fundamentally limited.

Understanding and predicting even the smallest details of a mRNA molecule’s protein folds is intrinsic to developing stronger predictions and, as a result, more effective mRNA-based vaccines.

Scientists hope to overcome the limitations inherent in the most powerful supercomputers and AI models by augmenting experiments with quantum technology. The researchers conducted multiple experiments using quantum simulation algorithms that relied on qubits — the quantum equivalent of a computer bit — to model molecules.

Initially using only 80 qubits (out of a possible 156) on the R2 Heron quantum processing unit (QPU),, the team employed a conditional value-at-risk-based variational quantum algorithm (CVaR-based VQA) — a quantum optimization algorithm modeled after certain techniques used to analyze complex interactions such as collision avoidance and financial risk assessment techniques — to predict the secondary protein structure of a 60-nucleotide-long mRNA sequence.

The previous best for a quantum-based simulation model, according to the study, was a 42-nucleotide sequence. The researchers also scaled the experiment by applying recent error-correction techniques to deal with the noise generated by quantum functions.

In the new preprint study, the team provisionally demonstrated the experimental paradigm’s effectiveness in running simulated instances with up to 156 qubits for mRNA sequences of up to 60 nucleotides. They also conducted preliminary research demonstrating the potential to employ up to 354 qubits for the same algorithms in noiseless settings.

Ostensibly, increasing the number of qubits used to run the algorithm, while scaling the algorithms for additional subroutines, should lead to more accurate simulations and the ability to predict longer sequences, they said.

They noted, however, that “these methods necessitate the development of advanced techniques for embedding these problem-specific circuits into the existing quantum hardware,” — indicating that better algorithms and processing architectures will be needed to advance the research.

Scientists have invented a new computing technology that enables multiple people to run programs on a quantum computer for the first time.

Dubbed "HyperQ," the new system is a type of virtualization technology that balances workloads by dividing a quantum computer's physical hardware into multiple isolated quantum virtual machines (qVMs) that are then tasked by an intelligent scheduler.

This scheduler operates "like a master Tetris player" that packs multiple qVMs together to run simultaneously on different parts of a single machine, Columbia representatives said in a statement. The end result is a single quantum computer capable of supporting multiple users running different applications. The scientists published their findings in July, in a new study which featured in the 19th USENIX Symposium on Operating System Design and Implementation (OSDI '25).

"HyperQ brings cloud-style virtualization to quantum computing," study coauthor Jason Nieh, professor of computer science at Columbia Engineering, said in the statement. "It lets a single machine run multiple programs at once — no interference, no waiting in line."

Efficiency through virtualization

Typical gate-based quantum computers are expensive compared to their binary counterparts. According to data from Quantum Zeitgeist, the research and development costs for a small-scale quantum computing system range from $10 million to $15 million. That’s before wecount the costs of upkeep, which are estimated at more than a million dollars per year, with software and programming development on top.

Related: 'A first in applied physics': Breakthrough quantum computer could consume 2,000 times less power than a supercomputer and solve problems 200 times faster

Despite the high development and operational costs, most quantum computers are usually only capable of supporting single-user operations due to the intrinsically interconnected nature of qubits — the quantum equivalent of classical computer bits — they comprise.

The researchers took inspiration from the virtualization technology that powers modern cloud computing services such as Amazon Web Services (AWS) and Microsoft Azure. In a classical computing virtual machine (VM) environment, a software layer called a hypervisor or Virtual Machine Monitor allocates unused resources to individual VMs that run entirely independent of one another.

In a quantum environment, however, computer scientists have to take into account "noise" in the quantum signal that could propagate throughout the system. HyperQ gets around this problem by isolating each qVM with a "buffer" of qubits that remain inactive, thus negating the potential for noisy "crosstalk."

"Previous efforts required specialized compilers and needed to know exactly which programs would run together ahead of time," said lead author of the paper Runzhou Tao, former doctoral student at Columbia’s Software Systems Laboratory. "Our approach works dynamically with existing quantum programming tools, which is far more flexible and practical for real-world use."

Dynamic multiprogramming

Quantum programs typically execute via a predictable pattern of qubits. HyperQ determines the optimum time slots for each user request and allocates resources across both time and space by determining which qubits will be necessary for each request and how long they’ll be active, the researchers said in the study.

This might sound like a simple concurrent scheduling task, but previous machine management systems required users to queue up so the system could precompile their requests for execution. HyperQ introduces a concept called “dynamic multiprogramming”, in which usage is streamlined, with programs allowed to be compiled independently for different-sized qVMs.

The team tested its HyperQ software layer on IBM’s Brisbane quantum computer, a 127-qubit gate-based system built on the Eagle chipset. According to the research, HyperQ reduced average user wait times by up to 40 times, lowering project turnaround times from "days to mere hours." It also enabled up to a tenfold increase in the number of quantum programs executed.

Going forward, the team intends to expand HyperQ to function across the gamut of quantum computing architectures, including machines made by manufacturers other than IBM.

Scientists have achieved the lowest quantum computing error rate ever recorded — an important step in solving the fundamental challenges on the way to practical, utility-scale quantum computers.

In research published June 12 in the journal APS Physical Review Letters, the scientists demonstrated a quantum error rate of 0.000015%, which equates to one error per 6.7 million operations.

This achievement represents an improvement of nearly an order of magnitude in both fidelity and speed over the previous record of approximately one error for every 1 million operations — achieved by the same team in 2014.

The prevalence of errors, or "noise," in quantum operations can render a quantum computer's outputs useless.

This noise comes from a variety of sources, including imperfections in the control methods (essentially, problems with the computer's architecture and algorithms) and the laws of physics. That's why considerable efforts have gone into quantum error correction.

While errors related to natural law, such as decoherence (the natural decay of the quantum state) and leakage (the qubit state leaking out of the computational subspace), can be reduced only within those laws, the team's progress was achieved by reducing the noise generated by the computer's architecture and control methods to almost zero.

Related: Scientists make 'magic state' breakthrough after 20 years — without it, quantum computers can never be truly useful

"By drastically reducing the chance of error, this work significantly reduces the infrastructure required for error correction, opening the way for future quantum computers to be smaller, faster, and more efficient," Molly Smith, a graduate student in physics at the University of Oxford and co-lead author of the study, said in a statement. "Precise control of qubits will also be useful for other quantum technologies such as clocks and quantum sensors."

Record-low quantum computing error rates

The quantum computer used in the team's experiment relied on a bespoke platform that eschews the more common architecture that uses photons as qubits — the quantum equivalent of computer bits — for qubits made of "trapped ions."

The study was also conducted at room temperature, which the researchers said simplifies the setup required to integrate this technology into a working quantum computer.

Whereas most quantum systems either deploy superconducting circuits that rely on "quantum dots" or employ the use of lasers — often called "optical tweezers" — to hold a single photon in place for operation as a qubit, the team used microwaves to trap a series of calcium-43 ions in place.

With this approach, the ions are placed into a hyperfine "atomic clock" state. According to the study, this technique allowed the researchers to create more "quantum gates," which are analogous to the number of “quantum operations” a computer can perform, with greater precision than the photon-based methods allowed.

Once the ions were placed into a hyperfine atomic clock state, the researchers calibrated the ions via an automated control procedure that regularly corrected them for amplitude and frequency drift caused by the microwave control method.

In other words, the researchers developed an algorithm to detect and correct the noise produced by the microwaves used to trap the ions. By removing this noise, the team could then conduct quantum operations with their system at or near the lowest error rate physically possible.

Using this method, it is now possible to develop quantum computers that are capable of conducting single-gate operations (those conducted with a single qubit gate as opposed to a gate requiring multiple qubits) with nearly zero errors at large scales.

This could lead to more efficient quantum computers in general and, per the study, achieves a new state-of-the-art single-qubit gate error and the breakdown of all known sources of error, thus accounting for most errors produced in single-gate operations.

This means engineers who build quantum computers with the trapped-ion architecture and developers who create the algorithms that run on them won't have to dedicate as many qubits to the sole purpose of error correction.

By reducing the error, the new method reduces the number of qubits required and the cost and size of the quantum computer itself, the researchers said in the statement.

This isn't a panacea for the industry, however, as many quantum algorithms require multigate qubits functioning alongside or formed from single-gate qubits to perform computations beyond rudimentary functions. The error rate in two-qubit gate functions is still roughly 1 in 2,000.

While this study represents an important step toward practical, utility-scale quantum computing, it doesn't address all of the "noise" problems inherent in complex multigate qubit systems.

In a world first, scientists have demonstrated an enigmatic phenomenon in quantum computing that could pave the way for fault-tolerant machines that are far more powerful than any supercomputer.

The process, called "magic state distillation," was first proposed 20 years ago, but its use in logical qubits has eluded scientists ever since. It has long been considered crucial for producing the high-quality resources, known as "magic states," needed to fulfill the full potential of quantum computers.

Magic states are quantum states prepared in advance, which are then consumed as resources by the most complex quantum algorithms. Without these resources, quantum computers cannot tap into the strange laws of quantum mechanics to process information in parallel.

Magic state distillation, meanwhile, is a filtering process by which the highest quality magic states are "purified" so they can be utilized by the most complex quantum algorithms.

This process has so far been possible on plain, error-prone physical qubits but not on logical qubits — groups of physical qubits that share the same data and are configured to detect and correct the errors that frequently disrupt quantum computing operations.

Because magic state distillation in logical qubits has not so far been possible, quantum computers that use logical qubits have not been theoretically able to outpace classical machines.

Related: What is quantum superposition and what does it mean for quantum computing?

Now, however, scientists with QuEra say they have demonstrated magic state distillation in practice for the first time on logical qubits. They outlined their findings in a new study published July 14 in the journal Nature.

"Quantum computers would not be able to fulfill their promise without this process of magic state distillation. It's a required milestone." Yuval Boger, chief commercial officer at QuEra, told Live Science in an interview. Boger was not personally involved in the research.

The path to fault-tolerant quantum computing

Quantum computers use qubits as their building blocks, and they use quantum logic — the set of rules and operations that govern how quantum information is processed — to run algorithms and process data. But the challenge is running incredibly complex algorithms while maintaining incredibly low error rates.

The trouble is that physical qubits are inherently "noisy," which means calculations are often disrupted by factors like temperature changes and electromagnetic radiation. That's why so much research has centered on quantum error correction (QEC).

Reducing errors — which occur at a rate of 1 in 1,000 in qubits versus 1 in 1 million, million in conventional bits — prevents disruptions and enables calculations to happen at pace. That's where logical qubits come in.

"For quantum computers to be useful, they need to run fairly long and sophisticated calculations. If the error rate is too high, then this calculation quickly turns into mush or to useless data," study lead author of the study Sergio Cantu, vice president of quantum systems at QuEra, told Live Science in an interview. "The entire goal of error correction is to lower this error rate so you could do a million calculations safely."

Logical qubits are collections of entangled physical qubits that share the same information and are based on the principle of redundancy. If one or more physical qubits in a logical qubit fail, the calculation isn't disrupted because the information exists elsewhere.

But logical qubits are extremely limited, the scientists said, because the error-correction codes applied to them can only run "Clifford gates" — basic operations in quantum circuits. These operations are foundational to quantum circuits, but they're so basic that they can be simulated on any supercomputer.

Only by tapping into high-quality magic states can scientists run "non-Clifford gates" and engage in true parallel processing. But generating these is extremely resource-intensive and expensive, and has thus far been unachievable in logical qubits.

In essence, relying on magic state distillation in physical qubits alone would never lead to quantum advantage. For that, we need to distill magic states in logical qubits directly.

Magic states pave the way for capabilities beyond supercomputing

"Magic states allow us to expand the number and the type of operations that we can do. So practically, any quantum algorithm that's of value would require magic states," Cantu said.

Generating magic states in physical qubits, as we have been doing, is a mixed bag — there are low-quality and high-quality magic states — and they need to be refined. Only then, can they fuel the most powerful programs and quantum algorithms.

In the study, using the Gemini neutral-atom quantum computer, the scientists distilled five imperfect magic states into a single, cleaner magic state. They performed this separately on a Distance-3 and a Distance-5 logical qubit, demonstrating that it scales with the quality of the logical qubit.

"A greater distance means better logical qubits. A Distance-2, for instance, means that you can detect an error but not correct it. Distance-3 means that you can detect and correct a single error. Distance-5 would mean that you can detect and correct up to two errors, and so on, and so on," Boger explained. "So the greater the distance, the higher fidelity of the qubit is — and we liken it to distilling crude oil into a jet fuel."

As a result of the distillation process, the fidelity of the final magic state exceeded that of any input. This proved that fault-tolerant magic state distillation worked in practice, the scientists said. This means that a quantum computer that uses both logical qubits and high-quality magic states to run non-Clifford gates is now possible.

"We're seeing sort of a shift from a few years ago," Boger said. "The challenge was: can quantum computers be built at all? Then it wasL can errors be detected and corrected? Us and Google and others have shown that, yes, that can be done. Now it's about: can we make these computers truly useful? And to make one computer truly useful, other than making them larger, you want them to be able to run programs that cannot be simulated on classical computers."

A new method of changing electronic states on demand could make electronics 1,000 times faster and more efficient, researchers say.

In a new study published 27 June in the journal Nature Physics, scientists discovered that controlled heating and cooling of a quantum material allows it to both insulate from and conduct electricity, depending on the temperature.

This material, named 1T-TaS₂, could potentially replace conventional silicon components in electronics, including laptops and smartphones. Quantum materials could accomplish the same tasks faster while taking up exponentially less room, the research team suggested.

If materials like 1T-TaS₂ were adopted for use in electronics, the amount of information they could process in a second would increase 1000-fold. "Processors work in gigahertz right now. The speed of change that this would enable would allow you to go to terahertz," Alberto de la Torre, a material physicist at Northeastern University and lead author of the study, said in a statement.

Thermal quenching

The technique the researchers used is called thermal quenching. It involves shining light on a material that has unique quantum properties when activated to increase its temperature. In the case of 1T-TaS₂, the activated trait is metallic conductivity.

This stable "hidden metallic state," as the researchers call it in the study, has previously been achieved, but only at cryogenically cold temperatures and for less than a second. The new research demonstrates that this property can be attained by temperature fluctuations at more practical temperatures — around -100 degrees Fahrenheit (-73 degrees Celsius), more than 250 degrees warmer than past experiments — the scientists said in the statement. What's more, the material 1T-TaS₂ can maintain its conductivity for months at a time with this method, which has never before been accomplished.

Related: Superfast diamond-laced computer chips now much closer to reality thanks to 'quantum breakthrough'

When light is removed, the material's temperature decreases and the 1T-TaS₂ falls back into its original insulating state. The result is comparable to a transistor — a semiconductor device in the majority of modern electronics that controls the flow of electricity. The advancement of transistors, in accordance with Moore's Law, is often credited with the shrinking of computers from machines that once occupied rooms to ones that can fit into your pocket.

Understanding how to control quantum materials has the potential to similarly transform electronics, Gregory Fiete, a theoretical physicist at Northeastern University and co-author of the paper, said in the statement.

"What we're shooting for is the highest level of control over material properties," he said. "We want it to do something very fast, with a very certain outcome, because that's the sort of thing that can be then exploited in a device."

"There's nothing faster than light"

Finding a way to switch between states of conductivity at higher temperatures is a game-changer for eventually replacing silicon-based technology, Fiete explained. Traditional silicon semiconductors contain many densely-packed logic components, which has physical limitations.

Because this new technique combines both conductive and insulating properties into a single object, quantum materials could accomplish the same tasks as silicon components while using much less space. "We eliminate one of the engineering challenges by putting it all into one material," he said.

Thermal quenching may also increase computing speeds because it relies on light to control conductivity. "Everyone who has ever used a computer encounters a point where they wish something would load faster," Fiete added. "There's nothing faster than light, and we're using light to control material properties at essentially the fastest possible speed that's allowed by physics."

This research opens up a new future for electronics, one where engineers can have instant control over a material's properties. "We're at a point where in order to get amazing enhancements in information storage or the speed of operation, we need a new paradigm," Fiete said. "That's what this work is really about."

Scientists have demonstrated that a photonic qubit — a quantum bit powered by a particle of light — can detect and correct its own errors while running at room temperature. They say it is a foundational step toward scalable quantum processors.

In a new study published June 4 in the journal Nature, researchers at Canadian quantum computing startup Xanadu created a so-called "Gottesman–Kitaev–Preskill" (GKP) state directly on a silicon chip.

GKP states are a type of quantum state that spreads information across multiple photons in a pattern that enables small errors to be spotted and corrected. This means that each qubit is capable of correcting itself, without needing to be bundled into large arrays of redundant qubits — a common requirement in today’s error-correction methods.

It marks the first time this type of error-resistant quantum state has been generated using a process compatible with conventional chip manufacturing, the scientists said.

The breakthrough suggests that error-correcting quantum states could be produced with the same tools used to manufacture conventional computer chips — bringing reliable, room-temperature quantum hardware a step closer to reality.

The qubit-cooling conundrum

Quantum computers work very differently from the classical machines we use today. Classical computers store information in binary bits, represented as either 1s or 0s. Quantum systems, meanwhile, use qubits that can exist in a "superposition" of both states. This enables them to solve complex calculations in parallel, and they can one day perform far beyond the reach of conventional systems.

But qubits are notoriously fragile. Even the smallest fluctuations in temperature, electromagnetic radiation or environmental noise can disrupt a qubit’s state and corrupt its data.

To guard against this, many quantum systems operate at temperatures close to absolute zero (minus 459.67 degrees Fahrenheit or minus 273.15 degrees Celsius) using complex cooling systems to maintain "coherence" — the fragile quantum connection through which qubits perform calculations.

Related: Coldest-ever qubits could lead to faster quantum computers

While this cooling helps preserve quantum information, it also makes quantum computers bulky, expensive and impractical to scale. Xanadu’s solution seeks to address this by using photons — particles of light that don’t require deep cooling — to build qubits that run on silicon chips at room temperature.

The team’s GKP demonstration tackles another key challenge: quantum error correction. Most quantum systems today rely on groupings of multiple physical qubits that work together to detect and fix errors, known as a "logical qubit." Xanadu’s photonic qubit sidesteps this by handling correction within each individual qubit, simplifying the hardware and paving the way for more scalable designs.

"GKP states are, in a sense, the optimal photonic qubit, since they enable logic gates and error correction at room temperature and using relatively straightforward, deterministic operations," Zachary Vernon, CTO of hardware at Xanadu, said in a statement.

"This demonstration is an important empirical milestone showing our recent successes in loss reduction and performance improvement across chip fabrication, component design and detector efficiency."

The result builds on Xanadu’s earlier development of Aurora, a modular quantum computing platform that connects multiple photonic chips using optical fiber. While Aurora addressed the challenge of scaling across a network, this new chip focuses on making each qubit more robust — a critical requirement for building fault-tolerant systems.

Xanadu representatives said the next challenge was reducing optical loss, which happens when photons are scattered or absorbed as they travel through the chip’s components.

Scientists say they have made a breakthrough after developing a quantum computing technique to run machine learning algorithms that outperform state-of-the-art classical computers.

The researchers revealed their findings in a study published June 2 in the journal Nature Photonics.

The scientists used a method that relies on a quantum photonic circuit and a bespoke machine learning algorithm.

Using only two photons, the team's technique successfully demonstrated increased speed, accuracy and efficiency over standard classical computing methods for running machine learning algorithms.

The scientists say this is one of the first times quantum machine learning has been used for real-world problems and provides benefits that cannot be simulated using binary computers. Furthermore, due to its novel architecture, it could be applied to quantum computing systems featuring only a single qubit, they said.

Unlike many existing methods for achieving speedup through hybrid quantum-classical computing techniques, this new method doesn't require entangled gates. Instead, it relies on photon injection.

Related: 'The science is solved': IBM to build monster 10,000-qubit quantum computer by 2029

Essentially, the team used a femtosecond laser — a laser that emits light in extremely short pulses measured in femtoseconds (10⁻¹⁵ seconds) to write on a borosilicate glass substrate to classify data points from a dataset. The photons were then injected in six distinct configurations, which were processed by a hybrid quantum-binary system.

The scientists determined where the photonic measurements outperformed those conducted via classical computing by measuring how long it took the photons to complete the quantum circuit. They then isolated the processes where quantum processing provided benefit and compared the results to the classical outputs.

The researchers found that experiments run using the photonic quantum circuit were faster, more accurate and more energy-efficient than those conducted using only classical computing techniques. This boosted performance applies to a special class of machine learning called "kernel-based machine learning" that can have myriad applications across data sorting.

While deep neural networks have become an increasingly popular alternative to kernel methods for machine learning over the past decade, kernel-based systems have seen a resurgence in the past few years due to their relative simplicity and advantages when working with small datasets.

The team's experiment could lead to more efficient algorithms in the fields of natural language processing and other supervised learning models.

Perhaps most importantly, the study showcases a novel method for identifying tasks that quantum computers excel at in hybrid computer systems.

The researchers say the techniques used are scalable, meaning they could lead to even better performance as the number of photons or qubits increases. This could, in turn, make it possible to develop machine learning systems capable of exceeding the limits of today's models, which increasingly face power consumption limitations due to the massive energy requirements needed to process data via electronics.

The researchers claim their techniques will "open the door to hybrid methods in which photonic processors are used to enhance the performance of standard machine learning methods."

Scientists have developed a new type of computer chip that removes a major obstacle to practical quantum computers, making it possible for the first time to place millions of qubits and their control systems on the same device.

The new control chip operates at cryogenic temperatures close to absolute zero (about minus 459.67 degrees Fahrenheit, or minus 273.15 degrees Celsius) and, crucially, can be placed close to qubits without disrupting their quantum state.

"This result has been more than a decade in the making, building up the know-how to design electronic systems that dissipate tiny amounts of power and operate near absolute zero," lead researcher David Reilly, professor at the University of Sydney Nano Institute and School of Physics, said in a statement.

The scientists described the result as a "vital proof of principle" for integrating quantum and classical components in the same chip — a major step toward the kind of practical, scalable processors needed to make quantum computing a reality. The researchers published their findings June 25 in the journal Nature.

Qubits are the quantum equivalent of binary bits found in today's classical computers. However, where a classical bit can represent either 0 or 1, a qubit can exist in a "superposition" of both states. This enables quantum computers to perform multiple calculations in parallel, making them capable of solving problems far beyond the reach of today's computers.

Related: Quantum computers that are actually useful 1 step closer thanks to new silicon processor that could pack millions of qubits

Spin qubits, a type of qubit that encodes information in the spin state of an electron, have piqued the interest of scientists because they can be built using complementary metal-oxide-semiconductor (CMOS) technology.

This is the same process used to fabricate the chips found inside modern smartphones and PCs. In theory, this makes spin qubits much easier to produce at scale as it slips into normal manufacturing methods.

Other quantum computers use different types of qubits, including superconducting, photonic or trapped-ion qubits. But unlike these other types, spin qubits can be made on a massive scale using existing equipment.

However, spin qubits need to be kept at temperatures below 1 kelvin (just above absolute zero) to preserve "coherence." This is a qubit’s ability to maintain superposition and entanglement over time, and what is needed to unlock the parallel processing power that makes quantum computing so promising. Spin qubits also need electronic equipment to measure and control their activity.

"This will take us from the realm of quantum computers being fascinating laboratory machines to the stage where we can start discovering the real-world problems that these devices can solve for humanity," Reilly added.

The road to a single million-qubit chip

Integrating the electronics required to control and measure spin qubits has long posed a challenge, as even small amounts of heat or electrical interference can disrupt the qubits' fragile quantum state.

But this new, custom CMOS chip is designed to operate in cryogenic environments and at ultra-low power levels, meaning it can be integrated onto a chip alongside qubits without introducing thermal or electrical noise that would otherwise interrupt coherence.

In tests, the researchers ran single-gate and two-qubit gate operations with the control chip positioned less than 1 millimeter (0.04 inches) from the qubits. The control chip introduced no measurable electrical noise and caused no drop in accuracy, stability or coherence, the researchers said.

Additionally, the control chip consumed just 10 microwatts (0.00001 watts) of power in total, with the analogue components — used to control the qubits with electrical pulses — using 20 nanowatts (0.00000002 watts) per megahertz.

"This validates the hope that indeed qubits can be controlled at scale by integrating complex electronics at cryogenic temperatures," Reilly said.

"This will take us from the realm of quantum computers being fascinating laboratory machines to the stage where we can start discovering the real-world problems that these devices can solve for humanity," he added.

"We see many further diverse uses for this technology, spanning near-term sensing systems to the data centres of the future."

The findings could prompt more researchers to explore the power of spin qubits.

"Now that we have shown that milli-kelvin control does not degrade the performance of single- and two-qubit quantum gates, we expect many will follow our lead," study co-author Kushal Das, senior hardware engineer at Emergence Quantum and a researcher at the University of Sydney who designed the chip, said in the statement.

"Fortunately for us, this is not so easy but requires years to build up the know-how and expertise to design low-noise cryogenic electronics that need only tiny amounts of power."

Quantum computers capable of outperforming today’s fastest supercomputers may not need to be as large or power-hungry as we thought, researchers at Canadian startup Nord Quantique say.

The company has built a quantum bit (qubit) with built-in error correction, eliminating the need for the large clusters of physical qubits typically required for fault-tolerant quantum computing.

Nord Quantique plans to scale this design into a 1,000-logical-qubit machine by 2031. The system would be compact enough to fit inside a data center and require far less energy than current platforms, researchers said.

The announcement follows a 2024 milestone in which the company demonstrated a working prototype of its "bosonic qubit" — a device that integrates quantum error correction directly into its hardware. In a statement, Nord Quantique representatives described the new architecture as "a first in applied physics" and a practical route toward scalable, utility-grade quantum machines. The breakthrough addresses a longstanding challenge in quantum computing: maintaining the integrity of quantum information over time.

Quantum bits are extremely sensitive to heat, vibration and electromagnetic interference — even when cooled close to absolute zero (–460°F, or –273°C). Most quantum platforms address this using quantum error correction, which combines many physical qubits to form a single logical unit capable of absorbing and correcting errors through redundancy, so that any single failure doesn’t scrub the entire calculation.

Related: Quantum computers are here — but why do we need them and what will they be used for?

However, creating a single logical qubit traditionally requires dozens or even hundreds of physical qubits, significantly increasing the size, complexity and energy cost of a quantum computer. Nord Quantique’s system avoids this by using a single physical component to perform the role of a logical qubit.

Quantum computing in safe mode

At the core of the design is a superconducting aluminum cavity known as a bosonic resonator, cooled to near absolute zero. This cavity contains light particles (photons) that store quantum information in specific electromagnetic patterns formed within the resonator. These patterns, known as "modes," each represent a different way the field resonates inside the cavity, allowing the same quantum state to be encoded in parallel.

By distributing information across multiple modes within the same physical structure, the qubit can identify and correct certain types of interference. If one mode is disrupted, the others provide enough context to restore the correct state. This method, known as multimode encoding, gives each qubit internal fault tolerance, reducing the need for external error correction and enabling a 1:1 ratio between physical and logical qubits.

The researchers estimated that a 1,000-logical-qubit machine built on this architecture would occupy just 215 square feet (20 square meters) and consume only a fraction of the energy used by high-performance systems today.

They also calculated that a quantum computer built using their architecture could break a 830-bit RSA encryption key in an hour, consuming just 120 kilowatt-hours of energy. By comparison, a supercomputer would require nine days and 280,000 kilowatt-hours to solve the same problem, they said.

"The amount of physical qubits dedicated to quantum error correction has always presented a major challenge for our industry," Julien Camirand Lemyre, chief executive at Nord Quantique, said in a statement. "Multimode encoding allows us to build quantum computers with excellent error correction capabilities, but without the impediment of all those physical qubits."

To make the system more fault-tolerant, the researchers used a "bosonic code" called Tesseract code. This helps guard against common quantum faults such as bit flips, phase flips, control errors and leakage, where the qubit slips into a state that isn’t part of the system used to store and process information. Leakage is hard to correct because most error correction techniques only work inside the expected set of quantum states and can’t spot when something falls outside it.

To test the system’s reliability, the researchers ran repeated rounds of error correction and filtered out results where the qubit didn’t behave as intended.

About 12.6% of runs were filtered out, they said. In the remaining data, the qubit held its state through 32 rounds of error correction without measurable decay, suggesting that multimode encoding can preserve quantum information reliably under stable conditions.

Nord Quantique plans to release a 100-logical-qubit machine by 2029, with the full 1,000-qubit system scheduled for 2031. "Beyond their smaller and more practical size, our machines will also consume a fraction of the energy,” said Camirand Lemyre. "That makes them especially appealing to [high-performance computing] HPC centers where energy costs are top of mind."

Computer scientists say they’ve cracked the science behind error-correction in quantum computers thanks to new "4D codes."

Developed by Microsoft, the new codes were revealed in a blog post published June 19 and purport to address the problem of fault tolerance — arguably quantum computing’s biggest bottleneck.

All computers can produce errors. In classical computing, error correction is achieved by making multiple copies of every bit of information that’s sent. If one or more bits are lost or corrupted, the remaining bits still contain the original information.

Qubits, however, can’t be copied. They also cannot be measured without experiencing what’s called "collapse." This makes it much more challenging to detect and mitigate errors (which occur at a significantly higher rate than in classical bits) as they happen.

A typical quantum error-correction setup involves the addition of extra "physical" qubits to a system. These qubits are entangled with the "logical" qubits that typically carry quantum information. Instead of measuring the logical qubits, thus causing this collapse, scientists can check for errors by measuring the entangled physical qubits. This allows the computation process to continue.

Scientists typically employ 4D codes in the quantum error-correction process by recreating the topology of quantum processing surfaces on a four-dimensional lattice. This creates a self-correcting form of quantum memory.

Related: 'The science is solved': IBM to build monster 10,000-qubit quantum computer by 2029

The trouble is that most current error-correction techniques are either difficult to scale, resource-intensive, or both. The more physical qubits required to provide fault tolerance for a quantum system, and the more error-correction passes needed, the more energy is required for computation.

"Microsoft’s novel four-dimensional geometric codes require very few physical qubits per logical qubit, can check for errors in a single shot, and exhibit a 1,000-fold reduction in error rates," said technical fellow of advanced quantum development at Microsoft Quantum, Krysta Svore, in the blog post.

A twist to quantum error-correction

The findings, uploaded June 18 to the arXiv preprint database, center on putting a literal twist on the torus-shaped 4D geometric code used for error-correction in certain quantum computing systems.

The scientists developed geometric code that could be overlaid in a system to detect errors using a four-dimensional topography. This 4D code connects the sample space (where the correction codes run) to the operational space (where the qubits contain information) via entanglement.

It works in four dimensions using a mathematical expression that, essentially, allows entanglement points to make connections over the surface of a "torus," which can be imagined as a donut shape.

While 4D codes have been used to create self-correcting quantum memory in the past, their use here is considered novel because the researchers calculated a "twist" in the geometry that allows the same amount of code to cover the same amount of system space using fewer physical qubit entanglements.

By "twisting" the geometry, the 4D code overlay creates a larger representational space that reflects a greater portion of the quantum state of the actual qubits in use. Doing so allows researchers to detect errors in the code without disturbing the actual quantum processes occurring within the system.

The researchers ran their new “twisted” code on existing quantum computers and experimentally confirmed their theories in a separate preprint paper, published to the arXiv preprint server on June 13. Neither paper has yet been peer reviewed.

"Universal fault-tolerant quantum computers may be realized using 4D geometric codes, which are designed to enable efficiently realizing an increasing number of logical qubits with a modest number of physical qubits, while enabling low-depth logical cycles and universal fault tolerance," the scientists said in the study.

Furthermore, the researchers purportedly demonstrated a groundbreaking technique for "replacing" the atoms used as qubits when they’re lost. In certain quantum computing systems, qubits are created by snagging neutral atoms with laser tweezers and trapping them in place. During computations, these atoms can be lost or dropped.

The researchers say they could replace lost atoms mid-cycle using an atomic beam to force new atoms into the array without disrupting the calculations — a first, the scientists said in the study.

Based on the findings, the new 4D code family could represent the second breakthrough in quantum error-correction in as many weeks. On June 10, IBM made a similar statement when it announced that it had developed quantum error-correction techniques that will lead to the development of a demonstrably useful quantum computer by 2029.

Where IBM’s new method utilizes a top-down development approach that takes advantage of its bespoke hardware, Microsoft’s is built from the bottom up to address fault tolerance using an approach that may have other applications beyond the hardware and use-cases it was tested on.

IBM scientists say they have solved the biggest bottleneck in quantum computing and plan to launch the world's first large-scale, fault-tolerant machine by 2029.

The new research demonstrates new error-correction techniques that the scientists say will lead to a system 20,000 times more powerful than any quantum computer in existence today.

In two new studies uploaded June 2 and June 3 to the preprint arXiv server, the researchers revealed new error mitigation and correction techniques that sufficiently handle these errors and allow for the scaling of hardware nine times more efficiently than previously possible.

The new system, called "Starling," will use 200 logical qubits — made up of roughly 10,000 physical qubits. This will be followed by a machine called "Blue Jay," which will use 2,000 logical qubits, in 2033.

The new research, which has not yet been peer-reviewed, describes IBM's quantum low-density parity check (LDPC) codes — a novel fault-tolerance paradigm that researchers say will allow quantum computer hardware to scale beyond previous limitations.

"The science has been solved" for expanded fault-tolerant quantum computing, Jay Gambetta, IBM vice president of quantum operations, told Live Science. This means that scaling up quantum computers is now just an engineering challenge, rather than a scientific hurdle, Gambetta added.

Related: Google's 'Willow' quantum chip has solved a problem that would have taken the best supercomputer a quadrillion times the age of the universe to crack

While quantum computers exist today, they're only capable of outpacing classical computer systems (those using binary calculations) on bespoke problems that are designed only to test their potential.

One of the largest hurdles to quantum supremacy, or quantum advantage, has been in scaling up quantum processing units (QPUs).

As scientists add more qubits to processors, the errors in calculations performed by QPUs add up. This is because qubits are inherently "noisy" and errors occur more frequently than in classical bits. For this reason, research in the field has largely centered on quantum error-correction (QEC).

The road to fault tolerance

Error correction is a foundational challenge for all computing systems. In classical computers, binary bits can accidentally flip from a one to a zero and vice versa. These errors can compound and render calculations incomplete or cause them to fail entirely.

The qubits used to conduct quantum calculations are far more susceptible to errors than their classical counterparts due to the added complexity of quantum mechanics. Unlike binary bits, qubits carry extra "phase information."

While this enables them to perform computations using quantum information, it also makes the task of error correction much more difficult.

Until now, scientists were unsure exactly how to scale quantum computers from the few hundred qubits used by today's models to the hundreds of millions they theoretically need to make them generally useful.

But the development of LDPC and its successful application across existing systems is the catalyst for change, Gambetta said.

LDPC codes use a set of checks to detect and correct errors. This results in individual qubits being involved in fewer checks and each check involving fewer qubits than previous paradigms.

The key advantage of this approach is a significantly improved "encoding rate," which is the ratio of logical qubits to the physical qubits needed to protect them. By using LDPC codes, IBM aims to dramatically reduce the number of physical qubits required to scale up systems.

The new method is about 90% faster at conducting error-mitigation than all previous techniques, based on IBM research. IBM will incorporate this technology into its Loon QPU architecture, which is the successor to the Heron architecture used by its current quantum computers.

Moving from error-mitigation to error-correction

Starling is expected to be capable of 100 million quantum operations using 200 logical qubits. IBM representatives said this was roughly equivalent to 10,000 physical qubits. Blue Jay will theoretically be capable of 1 billion quantum operations using its 2,000 logical qubits.

Current models have about 5,000 gates (analogous to 5,000 quantum operations) using 156 logical qubits. The leap from 5,000 operations to 100 million will only be possible through technologies like LDPC, IBM representatives said in a statement. Other technologies, including those used by companies like Google, will not scale to the larger sizes needed to reach fault tolerance, they added.

To take full advantage of Starling in 2029 and Blue Jay in 2033, IBM needs algorithms and programs built for quantum computers, Gambetta said. To help researchers prepare for future systems, IBM recently launched Qiskit 2.0, an open-source development kit for running quantum circuits using IBM's hardware.

"The goal is to move from error mitigation to error correction," Blake Johnson, IBM's quantum engine lead, told Live Science, adding that "quantum computing has grown from a field where researchers are exploring a playground of quantum hardware to a place where we have these utility-scale quantum computing tools available."

Scientists in the U.K. have successfully connected two separate quantum processors, paving the way for a quantum internet and, potentially, quantum supercomputers.

Increasing the number of quantum bits (otherwise known as qubits) in a quantum computer has proven challenging, as quantum computers are "noisy" — they are sensitive to any interference from heat, movement or electromagnetism and fail much more often than bits in classical computing.

The more qubits there are in a quantum computer, the more complex the system becomes and the greater the risk of decoherence — the loss of quantum information — and the resources needed to prevent errors. That's why scientists are focusing on building more reliable qubits before scaling systems up to the millions of qubits needed for a genuinely useful quantum computer.

In a study published Feb. 5 in the journal Nature, scientists proposed working around this scalability problem by connecting separate quantum processors together using existing fiber optic cabling, thereby increasing the number of available qubits.

This is an important step in demonstrating the feasibility of distributed quantum computing (DQC), whereby quantum processors are connected together to perform calculations. DQC would enable multiple quantum processors to work together to solve increasingly complex problems in far less time than it would take classical supercomputers.

The scientists described how they connected two quantum processors – called Alice and Bob (not to be confused with the quantum computing company Alice & Bob) using a photonic network interface (optical fibers). Sending quantum algorithms across the photonic network interface allowed the two quantum processors to share resources and operate as a single entity.

Distributed computing of the future

By connecting the two processors like this, the scientists could also transmit photons, together with quantum information and, for the first time, a quantum algorithm. Such algorithms are the computational functions that enable quantum computers to solve problems. These were shared by exploiting the phenomenon of quantum entanglement between photons.

The quantum processors could also work together on the test problem using the Grover search algorithm — a quantum algorithm that is designed to find a “needle in a haystack”; searching for a certain piece of information in a large pool of unsorted data.

This breakthrough is key to cracking the scalability problem in quantum computing. Instead of a single machine containing millions of qubits, which would be massive and unwieldy, the new technique allows for computations distributed across many smaller processors. Using small modules of trapped-ion qubits linked by optical cables, it allows qubits in separate QPUs to be entangled.

An additional benefit of connecting processors in a DQC system is ease of maintenance, as modules can be upgraded or replaced without disrupting the rest of the system.

As there was only a 6.6 feet (2 meters) gap between the two quantum processing units (QPUs), future trials of this technology would need to expand the operating distance to ensure the connection remains stable over much longer distances. Quantum repeaters, which increase the range over which quantum information can be transmitted, may also be incorporated into future systems.

Adding more quantum processors would provide further proof that DQC would be a viable solution for building quantum supercomputers. In much the same way that today’s supercomputers are hundreds of classical processors connected together, it is theoretically possible to create a quantum supercomputer by linking quantum processors together over vast distances.

As a proof of concept, the experiment proved that DQC is viable. It also creates the foundations for a secure quantum internet, which could allow for a more secure method of transmitting information, as quantum processors in different locations could be used to build a secure communications network.

In a statement, David Lucas, the principal investigator of the research team and lead scientist for the UK Quantum Computing and Simulation Hub, said the team’s "experiment demonstrates that network-distributed quantum information processing is feasible with current technology."

However, Lucas admitted there's plenty of work to be done before quantum computers are available for practical applications.

"Scaling up quantum computers remains a formidable technical challenge that will likely require new physics insights as well as intensive engineering effort over the coming years," he said.

Quantum computing is expected to leave classical computing in the dust when it comes to solving some of the world’s most fiendishly difficult problems. The best quantum machines today have one major weakness, however — they are incredibly error-prone.

That’s why the field is racing to develop and implement quantum error-correction (QEC) schemes to alleviate the technology’s inherent unreliability. These approaches involve building redundancies into the way that information is encoded in the qubits of quantum computers, so that if a few errors creep into calculations, the entire computation isn't derailed. Without any additional error correction, the error rate in qubits is roughly 1 in 1,000 versus 1 in 1 million million in classical computing bits.

The unusual properties of quantum mechanics make this considerably more complicated than error correction in classical systems, though. Implementing these techniques at a practical scale will also require quantum computers that are much larger than today’s leading devices.

But the field has seen significant progress in recent years, culminating in a landmark result from Google’s quantum computing team last December. The company unveiled a new quantum processor called Willow that provided the first conclusive evidence that QEC can scale up to the large device sizes needed to solve practical problems.

"Its a landmark result in that it shows for the first time that QEC actually works," Joschka Roffe, an innovation fellow at The University of Edinburgh and author of a 2019 study into quantum error correction, told Live Science. "There's still a long way to go, but this is kind of the first step, a proof of concept."

Why do we need quantum error correction?

Quantum computers can harness exotic quantum phenomena such as entanglement and superposition to encode data efficiently and process calculations in parallel, rather than in sequence like classical computers. As such, the processing power increases exponentially the more qubits you add to a system for certain types of problems. But these quantum states are inherently fragile, and even the tiniest interaction with their environment can cause them to collapse.

That’s why quantum computers go to great lengths to separate their qubits from external disturbances. This is normally done by keeping them at ultra-low temperatures or in a vacuum — or by encoding them into photons that interact weakly with the environment.

Related: Quantum computers will be a dream come true for hackers, risking everything from military secrets to bank information. Can we stop them?

But even then, errors can creep in, and occur at much greater rates than in classical devices. Logical operations in Google’s state-of-the-art quantum processor fail at a rate of about 1 in 100, says Roffe.

"We have to find some way of bridging this gulf so that we can actually use quantum computers to run some of the really exciting applications that we've proposed for them," he said.

QEC schemes build on top of ideas developed in the 1940s for early computers, which were much more unreliable than today’s devices. Modern chips no longer need error correction, but these schemes are still widely used in digital communications systems that are more susceptible to noise.

They work by building redundancy into the information being transmitted. The simplest way to implement this is to simply send the same message multiple times, Roffe said, something known as a repetition code. Even if some copies feature errors, the receiver can work out what the message was by looking at the information that is most often repeated.

But this approach doesn’t translate neatly to the quantum world, says Roffe. The quantum states used to encode information in a quantum computer collapse if there is any interaction with the external environment, including when an attempt is made to measure them. This means that it's impossible to create a copy of a quantum state, something known as the "no-cloning theorem." As a result, researchers have had to come up with more elaborate schemes to build in redundancy.

What is a logical qubit and why is it so important?

The fundamental unit of information in a quantum computer is a qubit, which can be encoded into a variety of physical systems, including superconducting circuits, trapped ions, neutral atoms and photons (particles of light). These so-called "physical qubits" are inherently error-prone, but it’s possible to spread a unit of quantum information across several of them using the quantum phenomenon of entanglement.

This refers to a situation where the quantum states of two or more particles are intrinsically linked with each other. By entangling multiple physical qubits, it's possible to encode a single shared quantum state across all of them, says Roffe, something known as a "logical qubit." Spreading out the quantum information in this way creates redundancy, so that even if a few physical qubits experience errors, the overarching information is not lost.

However, the process of detecting and correcting any errors is complicated by the fact that you can’t directly measure the states of the physical qubits without causing them to collapse. "So you have to be a lot more clever about what you actually measure," Dominic Williamson, a research staff member at IBM, told Live Science. "You can think of it as measuring the relationship between [the qubits] instead of measuring them individually."

This is done using a combination of "data qubits" that encode the quantum information, and "ancilla qubits" that are responsible for detecting errors in these qubits, says Williamson. Each ancilla qubit interacts with a group of data qubits to check if the sum of their values is odd or even without directly measuring their individual states.

If an error has occurred and the value of one of the data qubits has changed, the result of this test will flip, indicating that an error has occurred in that group. Classical algorithms are used to analyze measurements from multiple ancilla qubits to pinpoint the location of the fault. Once this is known, an operation can be performed on the logical qubit to fix the error.

What are the main error-correction approaches?

While all QEC schemes share this process, the specifics can vary considerably. The most widely-studied family of techniques are known as "surface codes," which spread a logical qubit over a 2D grid of data qubits interspersed with ancilla qubits. Surface codes are well-suited to the superconducting circuit-based quantum computers being developed by Google and IBM, whose physical qubits are arranged in exactly this kind of grid.

But each ancilla qubit can only interact with the data qubits directly neighboring it, which is easy to engineer but relatively inefficient, Williamson said. It’s predicted that using this approach, each logical qubit will require roughly 1,000 physical ones, he adds.

This has led to growing interest in a family of QEC schemes known as low-density parity check (LDPC) codes, Williamson said. These rely on longer-range interactions between qubits, which could significantly reduce the total number required. The only problem is that physically connecting qubits over larger distances is challenging, although it is simpler for technologies like neutral atoms and trapped ions, in which the physical qubits can be physically moved around.

A prerequisite for getting any of these schemes working, though, says Roffe, is slashing the error rate of the individual qubits below a crucial threshold. If the underlying hardware is too unreliable, errors will accumulate faster than the error correction scheme can resolve them, no matter how many qubits you add to the system. In contrast, if the error rate is low enough, adding more qubits to the system can lead to an exponential improvement in error suppression.

The recent Google paper has provided the first convincing evidence that this is within reach. In a series of experiments, the researchers used their 105-qubit Willow chip to run a surface code on increasingly large arrays of qubits. They showed that each time they scaled up the number of qubits, the error rate halved.

"We want to be able to be able to suppress the error rate by a factor of a trillion or something so there's still a long way to go," Roffe told Live Science. "But hopefully this paves the way for larger surface codes that actually meaningfully suppress the error rates to the point where we can do something useful."

A startup has launched the first quantum device in the world that blends the potential of quantum computing with the convenience and integration of traditional high-performance computing (HPC).

Equal1 representatives unveiled Bell-1 on March 16 — a new six-qubit machine that can fit seamlessly into existing HPC environments like data centers, company representatives said in a statement.

The machine tips the scale at slightly more than 440 pounds (200 kilograms) but it's rack-mountable — meaning it can be mounted onto a physical rack in a data center — and it's roughly the same size as existing graphics processing unit (GPU) servers.

Unlike other quantum computers, the Bell-1 doesn't require specialized infrastructure to deploy, and it doesn't need additional equipment to be cooled to near-absolute zero.

Related: China achieves quantum supremacy claim with new chip 1 quadrillion times faster than the most powerful supercomputers

That's because it boasts its own self-contained, closed-cycle cryo cooling unit, which enables the system to operate at a remarkable 0.3 kelvin, or minus 459.13 degrees Fahrenheit (minus 272.85 degrees Celsius).

The machine makes use of the latest semiconductor fabrication techniques as well as purified silicon, which allows for a high level of control and long coherence times (a qubit's ability to exist in multiple states simultaneously, which is crucial for quantum algorithms and computations).

Rack-mountable quantum computing

The Bell-1's qubits are silicon-based, meaning they’re smaller than conventional qubits, and the chip at the heart of the machine incorporates quantum processor units (QPUs) with Arm CPUs — traditional processors known for their small size and efficiency — and neural processing units (NPUs) — specialized processors for accelerating machine learning and artificial intelligence (AI).

Incorporating all these elements onto a single chip eliminates the complex orchestration that would otherwise be necessary between classical and quantum computing elements. As long as you've got the space in a rack, all it requires is a standard electrical outlet: plug it in and it's ready to work, Equal1 representatives said.

The company's chip, called the UnityQ 6-Qubit Quantum Processing System, utilizes spin qubits, in contrast to many quantum computing platforms that rely on either trapped-ion or superconducting qubits. Silicon-based spin qubits are compact, leading to potentially higher qubit density, and could leverage existing semiconductor fabrication techniques, meaning more scalability.

The chip fitted into the Bell-1 also incorporates error correction, control and readout, while taking advantage of existing semiconductor infrastructure for reliability and scalability.

Although this first generation of the chip includes six qubits, the company wants to make more powerful versions with a higher qubit count. The Bell-1 is also future-proof in that early adopters can upgrade existing systems as new models are rolled out, rather than replacing them with new machines, company representatives added.

The Bell-1 builds on advances first published by the company in December 2024, which established new peak performance marks for silicon qubit arrays as well as quantum controller chips.

These included the world’s highest recorded single-qubit and two-qubit gate fidelity (meaning fewer errors) and gate speed (meaning faster operations). The platform also utilizes a specialized, AI-powered error correction system developed in partnership with Arm.

Scientists have discovered how to use a quantum material to tap into the power of magnetism to store quantum information — thanks to its capacity to support magnetic switching (when the magnetic polarization switches direction). They say it can lead to more viable quantum computing and sensing, thanks to much longer-lasting quantum states.

Chromium sulfide bromide is an unusual material that has been likened to filo pastry (thin, folded layers of pastry) thanks to its structure of just a few layers of atoms. Scientists consider it extremely promising for quantum devices because many of its properties can be used for any type of information storage. It can be used to store information using an electric charge, as photons (as light), through magnetism (through the electronic spin) and even via phonons — like vibrations from sound.One of the many ways in which chromium sulfide bromide could be used to store information is through excitons — quasi-particles that form when an electron and its hole become bound together. When a photon is moved from its grounded energy state, it effectively leaves behind a hole where it once was. Although they are separated, the photon and the hole remain paired together and become known as an exciton.

Previous research has highlighted how these excitons can sometimes form in a straight line in the material. But these excitons also exhibit unusual magnetic properties.

At temperatures less than 132 Kelvin (-222 degrees F or -141 degrees C), the material's layers are magnetized and the electrons are aligned,while the direction of the magnetic field switches for each layer in the material.

When chromium sulfide bromide is warmed to more than 132 K, the material loses its magnetization as the electrons can move in random directions. In this unmagnetized state, the excitons are no longer trapped and extend over multiple layers of the material.

However, when chromium sulfide bromide is only a single atom thick, the excitons are confined to a single dimension. When used in a quantum device, this restriction could allow quantum information in the excitons to be stored much longer than it would otherwise be, as the excitons are less likely to collide with each other and lose the information they carry through decoherence (the loss of quantum information due to interference).

Quantum information in one dimension

In the new study published Feb. 19 in the journal Nature Materials, scientists reported that they had produced excitons in chromium sulfide bromide by firing pulses of infrared light in 20 bursts lasting only 20 quadrillionths of a second (20 x 10^-15). They then used a second infrared laser to nudge the excitons into a higher energy state, before finding they had created two different variations of exciton when they should otherwise have had identical states of energy.

When the less energetic pulses were shot by lasers from different axes, the researchers discovered that the direction-dependent excitons could be confined to a single line or expanded into three dimensions. The change from unidimensional; to three-dimensional excitons accounted for how long the excitons could last without colliding with each other.

"The magnetic order is a new tuning knob for shaping excitons and their interactions. This could be a game changer for future electronics and information technology," said co-author of the study Rupert Huber, professor of experimental and applied physics at the University of Regensburg, Germany.

One of the key areas the research team wants to pursue next is to investigate whether these excitons could be converted to magnetic excitations in the electronic spin of the material. Were they to achieve this, it could provide a useful method for converting quantum information between different subatomic particles (photons, excitons and electrons).

Switching between magnetized and non-magnetized states could provide a fast method for converting photon and spin-based quantum information. The hope with chromium sulfide bromide is to harness all of its properties for use in future devices.

"The long-term vision is, you could potentially build quantum machines or devices that use these three or even all four of these properties: photons to transfer information, electrons to process information through their interactions, magnetism to store information, and phonons to modulate and transduce information to new frequencies," said co-author of the study Mackillo Kra, professor of electrical and computer engineering at the University of Michigan, in a statement.

Scientists have developed the world's first operating system designed for quantum computers, which could let quantum computers connect with each other, thereby paving the way for a quantum internet.

An operating system, such as Microsoft Windows or Apple iOS, is the program responsible for managing every other application on a computer. However, most quantum computers are designed and built for a specific function; for example, to run an experiment or simulation.

This limits the potential functionality of quantum computers and hampers their connectivity. There are also different types of quantum computers that use different kinds of quantum bits (qubits) to achieve quantum superposition in different ways.

But on March 12, scientists published a new study in Nature describing QNodeOS, an operating system for quantum computers that works with all kinds of machines irrespective of the type of qubits they use.

Such an operating system would enable multiple quantum computers to be connected together and controlled by the same central platform.

The future of quantum computing

QNodeOS operates by combining a classical network processing unit (CNPU), which is the logical element for initiating the execution of the code, with a quantum network processing unit (QNPU), which controls the quantum code.

Together, the CNPU and QNPU form the QNodeOS, which controls a separate quantum device, called the QDevice.

Related: Quantum computers are here — but why do we need them and what will they be used for?

The QDevice is quantum hardware-dependent technology responsible for executing quantum operations (gates, measurements and entanglements). There would need to be a QDevice for every quantum computer that the QNodeOS is required to operate.

A key component of the QNodeOS is the QDriver, which connects the QNodeOS to the QDevice. The QDriver is the only part of the QNodeOS that is quantum hardware-dependent. It translates the platform-independent quantum operations from QNodeOS into platform-dependent instructions and vice versa, thus enabling the QNodeOS to control different types of quantum computers. Executing a process also requires NetQASM — a universal, platform-independent instruction set architecture for quantum internet applications.

The scientists demonstrated the QNodeOS by connecting different quantum computers together (two made from processed diamonds with nitrogen vacancy centers and another made from electrically changed atoms) and running a test program, in a similar way to how a classical computer performs a calculation using cloud computing.

Further experimentation with the QNodeOS is required, like using more quantum computers of different types, as well as increasing the distance between them, the researchers noted in the study. The study highlighted that the architecture could be improved by having the CNPU and QNPU on a single system board, to avoid millisecond delays in their communication, rather than relying on two separate boards.

An operating system for quantum computers represents a major step forward in their development. One of the potential applications for a quantum computer operating system is for distributed quantum computing, as well as potentially laying the foundations for a quantum internet.

A new quantum computing benchmark has revealed the strengths and weaknesses of several quantum processing units (QPUs).

The benchmarking tests, led by a team at the Jülich Research Centre in Germany, compared 19 different QPUs from five suppliers – including IBM, Quantinuum, IonQ, Rigetti and IQM – to determine which chips were more stable and reliable for high-performance computing (HPC).

These quantum systems were tested both at different "widths" (the total number of qubits) as well as different "depths" for 2-qubit gates. The gates are operations that act on two entangled qubits simultaneously, and depth measures the length of a circuit – in other words, its complexity and execution time.

IBM's QPUs showed the greatest strength in terms of depth, while Quantinuum performed best in the width category (where larger numbers of qubits were tested). The QPUs from IBM also showed significant improvement in performance across iterations, particularly between the earlier Eagle and more recent Heron chip generations.

These results, outlined in a study uploaded Feb. 10 to the preprint arXiv database, suggest that the performance improvements can be attributed not only to better and more efficient hardware, but also improvements in firmware and the integration of fractional gates — custom gates available on Heron can reduce the complexity of a circuit.

However, the latest version of the Heron chip, dubbed IBM Marrakesh, did not demonstrate expected performance improvements, despite having half the errors per layered gate (EPLG) compared to the computing giant’s previous QPU, IBM Fez.

Beyond classical computing

Smaller companies have made relatively big gains, too. Importantly, one Quantinuum chip passed the benchmark at a width of 56-qubits. This is significant because it represents the ability of a quantum computing system to surpass existing classical computers in specific contexts.

Related: China achieves quantum supremacy claim with new chip 1 quadrillion times faster than the most powerful supercomputers

"In the case of Quantinuum H2-1, the experiments of 50 and 56 qubits are already above the capabilities of exact simulation in HPC systems and the results are still meaningful," the researchers wrote in their preprint study.

Specifically, the Quantinuum H2-1 chip produced results at 56 qubits, running three layers of the Linear Ramp Quantum Approximate Optimization Algorithm (LR-QAOA) — a benchmarking algorithm — involving 4,620 two-qubit gates.

"To the best of our knowledge, this is the largest implementation of QAOA to solve an FC combinatorial optimization problem on real quantum hardware that is certified to give a better result over random guessing," the scientists said in the study.

IBM’s Fez managed problems at the highest depth of the systems tested. In a test that included a 100-qubit problem using up to 10,000 layers of LR-QAOA (nearly a million two-qubit gates) Fez retained some coherent information until nearly the 300-layer mark. The lowest performing QPU in testing was the Ankaa-2 from Rigetti.

The team developed the benchmark to measure a QPU's potential to perform practical applications. With that in mind, they sought to devise a test with a clear, consistent set of rules. This test had to be easy to run, platform agnostic (so it could work the widest possible range of quantum systems) and provide meaningful metrics associated with performance.

Their benchmark is built around a test called the MaxCut problem. It presents a graph with several vertices (nodes) and edges (connections) then asks the system to divide the nodes into two sets so that the number of edges between the two subsets is maximal.

This is useful as a benchmark because it is computationally very difficult, and the difficulty can be scaled up by increasing the size of the graph, the scientists said in the paper.

A system was considered to have failed the test when the results reached a fully mixed state — when they were indistinguishable from those of a random sampler.

Because the benchmark relies on a testing protocol that’s relatively simple and scalable, and can produce meaningful results with a small sample set, it’s reasonably inexpensive to run, the computer scientists added.

The new benchmark is not without its flaws. Performance is dependent, for instance, on fixed schedule parameters, meaning that parameters are set beforehand and not dynamically adjusted during the computation, meaning they can’t be optimised. The scientists suggested that alongside their own test, "different candidate benchmarks to capture essential aspects of performance should be proposed, and the best of them with the most explicit set of rules and utility will remain."

Quantum computing company Alice & Bob has improved the reliability of its cat qubits, which could make tomorrow's quantum computers far more accurate.

Fault tolerance is a major challenge in quantum computing. This is because the qubits in quantum computers are "noisy" and susceptible to decoherence — the loss of quantum information due to interference from the external environment. Improving qubits' reliability by implementing fault-tolerant technologies has therefore been a key research area.

There has been a particular drive to suppress the error rates associated with bit-flipping (when a qubit switches the probabilities of measuring 0 or 1). But previously, this had been found to lead to increased errors with phase-flipping (when a qubit switches its probabilities of being positive or negative).

Cat qubits are a type of qubit that mimics the superposition principle of Schrödinger’s cat — a thought experiment which postulates that a cat in a box with a randomly activated poison might be considered as both alive and dead until it is directly observed.

"Cat qubits" are designed to reduce bit-flips, thereby reducing the resources required for error correction. Cat qubits have been studied by multiple research teams, with qubits created by Alice & Bob scientists even incorporated into the Ocelot Chip, manufactured by Amazon Web Services (AWS).

Previous Alice & Bob research has demonstrated that cat qubits could achieve a bit-flip lifetime of 138 milliseconds.

But in a new study uploaded Feb. 28 to the pre-print arXiv database, scientists outlined a new way to stabilize cat qubits, with better bit-flip protection of up to 160 times, equating to a cat qubit lifetime of 22 seconds. The effect on the phase-flip rate was minimal.

The team achieved this by compressing the quantum states of cat qubits such that there is a smaller overlap between the two states. For these squeezed cat qubits, they demonstrated a steep reduction in bit-flip error rate as photon numbers increased.

The technique demonstrated in this research is especially useful, as it does not require any modifications to the design of the circuit. "Squeezing" cat qubits will therefore make error correction less resource-intensive than previous methods.

The next stage in Alice & Bob’s research will aim to develop universal fault-tolerant quantum computing, where bit-flips and phase-flips can be efficiently managed. This could lead to practical applications in fields such as chemistry and materials science.

Researchers have observed an elusive quantum phenomenon that was first predicted more than 50 years ago. This process, which forms a new state of matter, may have ramifications for future quantum computing.

The phase, called a superradiant phase transition (SRPT), is the result of two independent groups of quantum particles beginning to fluctuate in a way that's both coordinated and collective, the scientists said in a new study published April 4 in the journal Science Advances.

In this case, the two groups of particles were iron ions and erbium ions inside a crystal. Researchers were able to induce the phenomenon by applying a magnetic field — over 100,000 times stronger than the Earth’s — to a crystal made of erbium, iron and oxygen after cooling it to -457 °F (-271.67 °C), temperatures nearing absolute zero.

Under those conditions, the team was able to observe unmistakable signatures of an SRPT within the crystal. Their observations exactly matched predictions of what an SRPT would look like according to a famous model formulated by Robert H. Dicke in 1954.

The so-called Dicke model was the first to describe the phenomenon of superradiance — where excited atoms emit light faster than normal atoms — and laid the groundwork for understanding the superradiant phase transition as a distinct state of matter arising from strong interactions between light and matter. It was further elaborated on by Klaus Hepp and Elliot H. Lieb in 1973 who formally demonstrated the existence of this phase transition.

Related: Government scientists discover new state of matter that's 'half ice, half fire'

"Originally, the SRPT was proposed as arising from interactions between quantum vacuum fluctuations — quantum light fields naturally existing even in completely empty space — and matter fluctuations," said co-lead author Dasom Kim, a doctoral student in applied physics at Rice University, in a statement. "However, in our work, we realized this transition by coupling two distinct magnetic subsystems — the spin fluctuations of iron ions and of erbium ions within the crystal."

Spin describes the angular momentum of an elementary particle or atom. It dictates the behavior in magnetic fields and is important for determining the statistical properties of collections of particles, which, in turn, influence the structure of matter and the nature of fundamental forces. When excitation created by thermal fluctuations, alternating magnetic fields or other sources causes a wave-like disturbance across a pattern of spins in a material, it's called a magnon.

In the past, SRPT was branded a "no-go theorem" because it violated a fundamental limitation of light-based systems. But creating a magnonic version of the phenomenon allowed the team to bypass this restriction. In their experiment, the iron ions’ magnons play the role normally occupied by vacuum fluctuations, and the erbium ions’ spins fill in for matter fluctuations.

Researchers were able to clearly observe the disappearance of one spin mode's energy signal and a shift in the other — unmistakable evidence of an SRPT.

"We established an ultrastrong coupling between these two spin systems and successfully observed a SRPT, overcoming previous experimental constraints," Kim said.

The unique characteristics of an SRPT could have important implications for a diverse number of quantum technologies. This is due to a phenomenon called quantum squeezing, where fluctuations are reduced in one measurable property of a quantum system below the standard quantum limit (though fluctuations increase in another property).

"Near the quantum critical point of this transition, the system naturally stabilizes quantum-squeezed states — where quantum noise is drastically reduced — greatly enhancing measurement precision," Kim said in the statement. "Overall, this insight could revolutionize quantum sensors and computing technologies, significantly advancing their fidelity, sensitivity and performance."

There are further advantages beyond the precision of quantum measurements and computations due to an SRPT stabilizing quantum squeezed states, as well. Because SRPT arises from the collective behavior of many quantum particles, it could provide a form of built-in protection against individual qubit errors and decoherence, which are major hurdles in current quantum computing. The synchronized behavior could lead to more robust and stable qubits with longer coherence times. It's also possible that the strong, coherent interactions within an SRPT could lead to faster gates (the building blocks of quantum algorithms).