By: David Hayes, Sr. Director of Computational Design and Integrated Software

Two recent announcements from Quantinuum and Google mark important progress toward fault-tolerant quantum computing.

Because both announcements focus on reducing errors, it may be tempting to compare the numbers directly. But these results are not the same. They measure different parts of the fault-tolerance challenge and answer fundamentally different technical questions.

This week, we reported a world-record error-correction result, using our error-correction architecture on Helios to deliver a logical fidelity of 99.9995%, equivalent to just five errors per million operations and representing a 100x reduction in errors compared to the best published result across any other modality—reported by Google Quantum AI in 2024.  

Last week, Google reported a 99.99% magic-state fidelity result using a new cultivation technique, citing a 40x reduction in errors.

As fault-tolerant quantum computing matures, understanding what is being measured becomes just as important as understanding the headline number itself. Google's work measured the fidelity of a resource used in certain fault-tolerant operations. Our work measured the fidelity of the logical computation that consumes those resources.

Across the published results, Quantinuum demonstrates state-of-the-art performance in fault-tolerance, combining record logical computational fidelity with substantial advantages in resource efficiency. Understanding starts with examining what each study measured, what it achieved, and the resources it required.

Quantinuum's World-Record Result: Measuring the Accuracy of a Logical Computation

This week’s result measured how accurately an error-corrected logical quantum system can execute a computation.

To achieve the result, we used a well-known “color code” within our error-correction architecture. The error rate is 10 times lower than Quantinuum’s own world record achieved last month and 100 times lower than the best published result across all other hardware modalities—the 2024 Google result.

The study also demonstrated that logical errors fell exponentially as code distance increased. Code distance is a key measure of how quantum information can be protected as an error-correction system scales.

Each increase tested in the study reduced the remaining logical error rate by a record factor of 12, demonstrating exponentially effective error suppression as the code grows, a critical capability for scalable fault-tolerant quantum computing.

Together, these results demonstrate how Quantinuum's highly accurate trapped-ion hardware and flexible logical architecture can drive rapid gains in computational accuracy. They also show that fault-tolerant performance can be achieved with substantially fewer physical qubits than competing modalities.

The result places Quantinuum within the target logical-performance range required for Apollo, our planned universal fault-tolerant quantum computer targeted for 2029.

Google's Result: Advancing Magic-State Preparation

Google's latest paper focused on a different aspect of fault tolerance: the preparation of high-fidelity magic states.

Magic states are an important resource required for certain operations in a fault-tolerant quantum computer. Generating them efficiently is widely recognized as one of the key engineering challenges facing large-scale fault-tolerant systems, particularly for certain modalities.

Google's work introduced a new approach known as cultivation, designed to improve the fidelity of magic-state preparation while reducing the resources required to produce those states. The study demonstrated a magic-state fidelity of 99.99% using a protocol that retained approximately 8% of attempts while discarding the remainder. This post-selection process was a key contributor to the reported 40x reduction in error rate.

The study did not measure the fidelity of a larger logical computation that incorporates those magic states into an end-to-end algorithm. Instead, it focused specifically on the creation and refinement of the resource itself.

That distinction is important because preparing a high-fidelity resource and demonstrating the performance of a logical computation are different milestones on the path to fault tolerance.

Directly Comparing Quantinuum and Google’s Magic-State Results

Quantinuum's latest logical computation result should not be compared directly with Google's latest magic-state result. It should be compared against the distance scaling result published by Google in 2024.

That said, we have published studies measuring an analogous magic-state capability examined in Google’s latest work.

In a 2025 study, we achieved 99.993% magic-state fidelity with an 85% retention rate, compared with the 99.99% fidelity and approximately 8% retention reported in Google’s latest work. Our result also required fewer physical qubits and fewer operational steps, resulting in significantly lower space-time overhead.

That combination of smaller qubit and operational steps footprint, along with substantially higher retention means fewer resources are consumed preparing each usable magic state.

As fault-tolerant systems scale, those efficiency advantages become increasingly important because they directly affect the amount of hardware required to perform useful computations.

The Bigger Picture

Taken together, these results establish Quantinuum’s leadership in demonstrated fault-tolerant performance: record logical computation fidelity, alongside a directly comparable magic-state result that achieves higher fidelity with substantially lower resource overhead.

That combination—computational accuracy and the efficiency with which it is achieved—is critical to delivering reliable answers to useful problems at a practical scale.

Ultimately, customers do not buy two-qubit gate fidelities or magic states. They buy answers.

Forward-Looking Statements

This blog post contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. These include statements about our technology roadmap, our expected logical performance, and the planned timing and capabilities of future systems, including Apollo. Words such as "target," "expect," "plan," and similar expressions identify forward-looking statements. These statements are based on our current expectations and involve risks and uncertainties that could cause actual results to differ materially. Those risks include challenges in developing, manufacturing, and scaling our quantum computing systems. For a discussion of these and other risks, see the "Risk Factors" section of our most recent Quarterly Report on Form 10-Q and our other filings with the SEC, available at ir.quantinuum.com. Forward-looking statements speak only as of the date of this post, and we undertake no obligation to update them except as required by law.

• Record accuracy: Using Quantinuum’s logical error-correction architecture, Helios achieved a logical memory fidelity of 99.9995%—just five uncorrectable errors per million operations—reducing logical memory errors tenfold compared with Quantinuum’s September record and more than 100-fold compared with the superconducting record.
• Errors fall exponentially as protection grows: Each tested increase in code distance reduced logical memory errors by an average factor of 12, and logical gate errors by an average factor of 9, demonstrating the exponential error suppression needed to scale toward longer, more reliable quantum computations.
• A flexible path to fault tolerance: Successive breakthroughs using different codes within the same logical architecture, on the same commercial Helios hardware, demonstrate how accuracy and qubit efficiency can be optimized as Quantinuum advances toward Apollo in 2029.

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Recently, we started making the case that the NISQ¹ era is coming to an end. At Quantinuum, we’ve already demonstrated numerous QEC codes, all the primitives needed for logical computation, steadily declining logical error rates, and full computations at the logical level.

To truly leave the NISQ era, though, we need to demonstrate a “megaquop” machine – a quantum computer that can run over a million operations error-free. With our latest result, we are on the cusp of this monumental achievement: using Quantinuum’s logical error-correction architecture, Helios achieved a logical memory fidelity of 99.9995%— or a million operations with only 5 uncorrectable errors, a new world record. This logical error rate is ten times lower than our September record and more than 100 times lower than the record achieved on a superconducting system.

Coming after September’s Helix-code demonstration on the same Helios hardware, this result shows how our flexible logical architecture and highly accurate trapped-ion technology support rapid improvements in accuracy.

These results indicate that an Apollo-scale machine could reach the fault-tolerant quantum advantage regime using 30x fewer physical qubits than other leading approaches, reinforcing our path to Apollo, our first universal fault-tolerant quantum computer.

A quantum memory that lasts

At Quantinuum, we don’t have delicate qubits that can only hold their state for milliseconds at a time. We’ve already shown quantum memories lasting over 20 seconds. However, this latest result takes it to a whole new level: we have demonstrated the most robust quantum memory ever recorded.

This latest result was enabled by something scientists call “distance scaling”, a key result in quantum error correction (QEC) theory: the prediction that error rates should be suppressed exponentially as longer distance codes (more physical qubits per logical qubit) are used.

In experiments using color codes of distance 3, 5 and 7, Helios (with code written in Guppy) reduced the logical failure rate by an average factor of 12 each time the code distance increased. At distance 7, the system achieved a logical fidelity of 99.9995% per syndrome-extraction cycle, equivalent to a failure probability of just 5 × 10⁻⁶. This nearly “six nines” result sets a new record for logical accuracy through quantum error correction.

To measure the failure rate of something that rarely fails, you must run it for a long time. This ensures that you get reasonable enough statistics to feel comfortable that your measured value isn’t just a statistical fluctuation (scientists call this 'averaging down’ the uncertainty in your measurement).

When you only get an error 5 times in a million rounds, this often means ‘averaging down’ for a long time to even see an error².

“We felt a mix of excitement and suspense every time the results came in. When errors are this rare, each one really matters."

- Dr. Adam Reed, Lead Physicist, Commercial Operations

More than a memory: high-fidelity logical gates

The team also explored logical computation, measuring 2-qubit Clifford gate fidelity at increasing code distance. In this case, the team measured a suppression factor Λ of 9 when stepping the code distance up from 3 to 5, proving again our hardware’s exponential error suppression³.

If one assumes a “Clifford + T” model of universal computation (with T gates teleported via magic state injection), then a fully fault tolerant processor built with this code will have three crucial components:

1. Magic state preparation
2. Two-qubit Clifford gates
3. Syndrome extraction gadgets.

This work demonstrates high accuracy implementation and below threshold scaling of the latter two, and we demonstrated the first in a separate paper (with the same code) back in 2025.  Teleporting T gates via magic state injection will require real-time decoding, which we first demonstrated back in 2021 and have been refining ever since – in fact, this work shows that this is viable for near-optimal decoders. There is still work to be done, however: nobody has yet demonstrated below threshold scaling for magic state preparation.

We’ve already put almost everything together in the Helix code. Soon, we will be integrating all these color-code components into increasingly large, reliable computations. Our ability to fully employ multiple codes is a testament to our powerful error correction architecture.

World-record logical fidelities come from world-leading physical fidelities‍

Distance scaling is great in theory, but it is rarely demonstrated because it only works when the underlying hardware noise falls below a certain threshold. This is known as the “threshold theorem.” Basically, until the physical layer has low enough noise levels, QEC will just add more noise than it takes away.

Helios’ incredible performance – with a ‘suppression rate’ Λ of 12 (for memory) and 9 (for Clifford gates) - is a clear demonstration of our world-leading physical fidelity, which puts us well below the ‘threshold’.

The rate Λ captures the exponential suppression in error rate with increasing code distance⁴: increasing the code distance once gives an 12x reduction in logical error, increasing the distance twice gives 144x, three times 1728x, and so on. In contrast, a machine with a Λ of 2 gives you a 2x reduction, then a 4x reduction, then 8x, and so on – a much slower gain. The magnitude of Λ is crucial for how well quantum computers can scale, and this is the largest Λ ever measured.

This effect shows up in the best error rates, too. The previous state of the art color-code error rate (at distance 5) was 0.819% per cycle. Helios, in contrast, showed an error rate of 0.0005% with a distance 7 code, a 1,638x improvement⁵. This massive improvement is due to our excellent physical fidelity, improved scaling behavior, and our ability to go to a longer distance code (in this case, d=7 vs d=5)⁶.

Comparing at the same code distance, the previous best demonstrated error rate was using a surface code. In this case, the researchers demonstrated an error rate of 0.0772% per cycle with a distance 7 code. In this case, Helios shows a 154x improvement⁷.

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Figure 4. Animation of logical qubit growth with increasing code distance. The white circles represent physical qubits, and the animation shows how logical qubits are formed at color code distances of 3, 5, and 7.

Our trapped-ion qubits lead to significant resource savings

On top of our new record in fidelity, the scaling with code distance reveals something very important: our QCCD⁸ approach is expected to reach utility scale with far less resources than other modalities.

This all comes down to the value of Λ, which describes how quickly error rates decrease with increasing code distance. The most comparable result, done in a superconducting architecture with the color code, had a Λ of 1.56, while Helios demonstrated a Λ of 12. This means that we can hit a ‘utility scale’ error rate, say 10^-12, with shorter distance codes - which translates to far less physical qubits.

Mathematically, this is a straightforward calculation: you take a known error rate at a given distance (call this LER_d), then you compute LER_d×Λ^(x-d)/2, then find whatever code distance is needed to get to your desired error rate.  

The plot above compares state-of-the-art color code implementations on Helios and a superconducting architecture. For an error rate of 10^-12, indicated with a horizontal black line, the superconducting system would need a code distance of 109 to achieve the target error rate, while our computers would only need a code distance of 21⁹.

Translating this into a physical qubit requirement to generate 100 logical qubits, the superconducting processor would need ~900,000 physical qubits while our QCCD processors would only need ~30,000, a ~30x resource savings. This also brings in timelines: building a 30,000-qubit processor is much more likely in the near term than a 900,000-qubit processor.

In our recent blog about the Helix QEC architecture, we pointed out how our targeted physical fidelities will keep advancing our logical fidelities. That holds here as well: with the physical fidelity improvements targeted for Sol and Apollo, we will see an improved LER_d and Λ, meaning that we can achieve target error rates at even shorter distance codes than above. This brings in timelines further: even fewer physical qubits will be needed.

Finally, we have access to codes that cost less qubits in the first place. These ‘high encoding rate’ codes require connectivity beyond what is available in fixed qubit superconducting systems. With our reconfigurable qubits, we can implement exotic entangling schemes that allow for a single physical qubit to be shared amongst several logical qubits, further improving resource estimates and bringing in the timelines to large scale utility.
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Our Sol system, coming next year, will continue to reduce these costs by moving us to a grid-shaped trap, shortening transport and cooling times, while enabling full connectivity at a larger scale.

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Figure 6. A qubit moves through a prototype grid trap, demonstrating the precise control and dynamic qubit transport that enable all-to-all connectivity in our QCCD architecture. This is footage from an operating device, not an artist’s rendering.

An important takeaway is that a lot of resource estimates that claim we will need X qubits or Y gates to do some algorithm are outdated; as they rely on assumptions about distance scaling and QEC codes that are not relevant to our QCCD architecture. With our high fidelity and full connectivity, we will be able to achieve target applications sooner than expected (compared to many external estimates), needing fewer qubits and fewer gates overall.

Helios as a pathfinder

Figure 7. Qubits are rearranged within a prototype grid trap using broadcast control signals, demonstrating our solution to quantum computing’s “wiring problem.” This approach dramatically reduces the number of control signals—or “wires”—needed to route qubits, overcoming a critical barrier to scaling into the megaquop regime. We first demonstrated this capability in 2024. The video shows real qubits in motion—it is not an artist’s rendering.

This result reflects more than the performance of an exceptional QPU. It is the product of a cross-functional, full-stack team working in close coordination, using Helios as a pathfinder for our next generation systems.

For example, one challenge every leading quantum computing platform faces is leakage: an unwanted process that takes the system out of the two qubit states used for computation. Leakage is especially damaging for QEC codes, which are built to fix errors within those two states and are not equipped to handle a qubit that has left them. Moreover, all subsequent operations on a leaked qubit quietly fail — a single leak can lead to a cascade of correlated errors which could result in a logical failure.

On Helios, the team built a mid-circuit fix: ‘leakage repump’, which returns leaked qubits back into the computational states as ordinary, correctable errors — improving our logical error rate by over an order of magnitude and enabling our distance-scaling demonstration.

Another example is ultra-efficient decoding. Syndrome data must be interpreted quickly enough to correct errors before the quantum information degrades. The team evaluated several approaches, including machine-learning decoders developed by Quantinuum’s AI team, ultimately demonstrating accurate decoding with sub-millisecond latency, which is 10-100x faster than needed to get corrections.

Both of these point to Helios operating in pathfinder mode: helping us understand how to extract the best performance from this generation of hardware, while translating those lessons into the next. That knowledge cannot come from simulation alone – we need to build and test real hardware to continue advancing. As Lead Physicist Dr. Adam Reed put it,  "When we push Helios to its limits, we often learn something the simulations didn't tell us."

By bringing together our theory, facilities, AI, and hardware teams, we pushed our trapped-ion hardware into a new performance regime—showing how our flexible logical architecture, tailored decoders, and highly accurate trapped-ion hardware work together to advance our path to Apollo in 2029.

¹ Noisy Intermediate-Scale Quantum
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² In practice, we ran for 580,000 shots (29k shots, each one having 20 rounds, and observed 3 failures), which gave us just enough statistical confidence to claim the ultimate error rate.
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³ As you can see in Figure 3, the suppression factor has error bars of +8 and -4, meaning it could be much better or closer to the 12x we measured in the other experiment.
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⁴ With the color code, only odd code distances are available.
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⁵ The previous state of the art demonstration of distance scaling with the color code (with a maximum distance of 5) showed an error rate of 1.1% per cycle, in which case Helios shows a 2,200x improvement.
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⁶ If you want to compare these demonstrations at the same distance, Helios showed an error rate of 0.004% with a distance 5 code, which is a 204x improvement.
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⁷ The previous state of the art demonstration of distance scaling with the surface code (with a maximum distance of 7) showed an error rate of 0.143% per cycle, in which case Helios shows a 286x improvement.
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⁸ Quantum Charge Coupled Device
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⁹ It might look like we need a shorter distance code, but recall that only odd code distances are available with the color code. To hit the teraquop regime, we need slightly more than a code distance of 19. The next highest distance is 21.

• Our team, in partnership with HQS Quantum Simulations, just published what we believe to be the most accurate¹ large-scale quantum simulation of Nuclear Magnetic Resonance (NMR) to date, pointing the way to new frontiers in computational chemistry.
• NMR is a widely used analytical technique that can require significant computational costs to interpret, costs that can balloon quickly on classical systems.
• In this result, the joint team ran an end-to-end simulation on System Model H2, reproducing key spectral features that previous demonstrations were unable to capture.

Nuclear magnetic resonance (NMR) spectroscopy is one of the most powerful analytical techniques in modern science. From identifying drug candidates to understanding battery materials, it allows researchers to probe the local atomic structure of molecules and materials with remarkable precision.

Interpreting NMR experiments often requires simulations that are just as challenging as the experiments themselves. As the number of interacting nuclear spins grows, the computational cost of simulating these systems increases exponentially on classical computers. Quantum computers, which naturally represent and evolve quantum states, offer a fundamentally different approach.

In our latest work, we demonstrate the most accurate large-scale digital NMR simulation performed on quantum hardware to date. Using Quantinuum's System Model H2, we carried out an end-to-end simulation of a classically challenging NMR experiment, reproducing key spectral features that previous hardware demonstrations were unable to capture.

Why simulate NMR?

NMR is a cornerstone of chemical analysis. Researchers use it to determine molecular structures, characterize new compounds, and study how atoms interact with one another.

These capabilities make NMR indispensable across industry. In pharmaceutical research, NMR helps identify and characterize drug candidates. In materials science, it reveals the local atomic environments that determine material properties. Battery researchers, for example, use NMR to study cathode materials such as lithium cobalt oxide, allowing them to monitor how the material changes during charging and discharging and ultimately improve battery performance.

In many cases, the experimental spectrum is only part of the story. Simulations help scientists interpret complex spectra by revealing which atomic interactions give rise to the observed peaks. They provide the link between an experimental measurement and the underlying molecular structure.

The classical challenge and the quantum solution

The difficulty lies in the physics.

An NMR experiment measures the dynamics of interacting nuclear spins. Every additional spin rapidly increases the size of the quantum state that must be represented (in the case of a spin-½ particle, every spin doubles the state). As a result, exact classical simulations become exponentially more expensive as the system size grows.

Today's best classical methods are remarkably sophisticated. Exact simulations are typically limited to systems of around 25 interacting spins, while advanced approximation techniques can often extend calculations to roughly 40–50 spins for many practical problems.

Those approximations have made classical NMR software extraordinarily effective for conventional liquid-state spectroscopy. In fact, our collaborators on this project are developing one of the leading classical simulation packages and noted that, despite the success of our quantum experiment, the current spectral resolution is still insufficient for routine use by practicing spectroscopists. Resolving the fine structure needed for many real-world analyses would require substantially longer simulations—and therefore much deeper quantum circuits than current hardware can yet support.

This highlights both the progress and the remaining challenge. Quantum computers are beginning to produce meaningful NMR spectra, but practical utility will require larger quantum computers and increased simulation depth.

In the longer term, interacting spin systems are among the most natural applications for quantum computers. Instead of storing the exponentially large quantum state explicitly, a quantum computer represents it directly in its physical qubits. For spin-½ nuclei, the mapping is particularly efficient: each nuclear spin corresponds directly to a single qubit. Higher-spin nuclei require only a small number of additional qubits.

This does not eliminate every computational challenge—longer simulations still require deeper quantum circuits—but it avoids the exponential memory bottleneck that limits classical simulation.

For NMR, this makes quantum simulation an especially compelling long-term application.

Our experiment

Working with collaborators at HQS Quantum Simulations, we implemented a complete quantum workflow for simulating an NMR experiment on Quantinuum's H2 trapped-ion quantum computer.

Rather than stopping at Hamiltonian simulation alone, we reproduced the entire computational pipeline:

• constructing the nuclear spin Hamiltonian,
• compiling efficient quantum circuits,
• simulating the spin dynamics through Trotterized real-time evolution,
• applying targeted error-suppression techniques,
• reconstructing the free induction decay, and
• generating the final NMR spectrum through Fourier analysis.

The benchmark molecule, 1,2-di-tert-butyl-diphosphane, is a well-known challenge for NMR simulation. After applying hardware-efficient model reduction, we simulated an effective 21-spin Hamiltonian using a 42-qubit (21 system qubits and 21 ancilla qubits) computation on System Model H2.

Our deepest circuits reached over 1,400 two-qubit gates and simulated 70 Trotter steps, corresponding to approximately 29 ms of physical evolution time.

Most importantly, the resulting spectrum reproduced the key benchmark features of the classical reference calculation, including a characteristic double-peak structure that previous quantum hardware demonstrations had failed to recover.

This represents the most complete hardware demonstration of digital NMR simulation reported to date.

Why trapped ions mattered

Achieving this result depended not only on the quantum algorithm but also on the underlying hardware.

Large Hamiltonian simulations require long, high-fidelity quantum circuits. Quantinuum's trapped-ion architecture provides all-to-all qubit connectivity, high-fidelity gates, and effective error-suppression techniques that allowed us to preserve the spectroscopic features throughout the computation.

The close agreement between hardware, emulator, and classical reference calculations demonstrates that deep quantum simulations of chemically meaningful systems are becoming increasingly feasible on today's hardware.

Looking ahead

To make quantum NMR genuinely useful for practicing spectroscopists, future systems will need substantially deeper circuits.

Our experiment simulated approximately 29 ms of evolution time, producing spectral features with a resolution of roughly 0.07 ppm, a very promising start.

Reaching that scale will require continued improvements in hardware fidelity, error correction, and quantum algorithms.

Nevertheless, this work demonstrates that digital quantum simulation of realistic NMR experiments is no longer purely theoretical. It establishes a practical end-to-end workflow, validates key algorithmic techniques, and shows that quantum computers can already reproduce chemically meaningful spectral signatures.

As quantum hardware continues to improve, applications such as molecular spectroscopy, materials characterization, and chemical simulation are becoming increasingly realistic targets for practical quantum computing.

¹ Based on a study of existing literature

• In a recent independent study by the Julich Supercomputing Center, Quantinuum’s trapped-ion systems outperformed superconducting processors in tests of fault tolerant operations.
• Testing small chunks of circuits that would be used in larger QEC workflows, Quantinuum’s Helios demonstrated an approximately 10x lower effective hardware error compared to superconducting systems.
• Quantinuum’s flexible connectivity enabled tests across three error-correcting code families, compared to just one for superconducting systems, illustrating the expanding options for advancing fault tolerance with our QCCD architecture.

The demands of fault tolerance are bringing Quantinuum’s advantages into sharper focus.

In a new independent study from the Jülich Supercomputing Centre, Quantinuum’s Helios demonstrated an approximately order-of-magnitude advantage over superconducting hardware in tests of operations essential to quantum error correction. More precisely, Helios’ effective hardware error was approximately 13 times lower at 30 data qubits and eight times lower at 50 data qubits than the comparable superconducting result.

The advantage extended to architectural flexibility. Quantinuum’s systems supported tests across three error-correcting code families, while connectivity and control restrictions limited implementation to only one on the superconducting devices evaluated.

These findings build on earlier independent research by the same organization highlighting Quantinuum’s physical-level performance. As we have argued, the NISQ era is coming to an end. The demands of error correction are bringing our architectural differences into sharper focus—and this study provides further evidence of Quantinuum’s advantage.

Error correction exposes performance gaps

A fault-tolerant quantum computer must repeatedly detect and correct errors to protect the computation in progress. That requires reliable mid-circuit measurements, conditional operations, and real-time coordinated scheduling.

In a direct comparison, introducing mid-circuit measurement caused substantially greater degradation on the superconducting processors compared to the Quantinuum systems.

That difference matters. Mid-circuit measurement must be repeated throughout fault-tolerant computations. Their (potential) performance penalty directly affects how much computation a machine can sustain.

Helios demonstrated strong performance under those demands, extending its advantage beyond the physical layer into circuits exercising essential error-correction capabilities.

Connectivity expands the options

The study also highlighted how architectural restrictions affect which error-correction structures hardware can implement directly.

On Quantinuum’s systems, researchers ran tests exploring the surface-code, triangular color-code, and a bivariate-bicycle qLDPC code. In contrast, the superconducting devices evaluated were only able to explore the surface code due to device constraints.

This is because Quantinuum’s QCCD architecture offers greater flexibility: mobile qubits enable codes that require higher connectivity than is allowed on traditional superconducting processors. Ultimately, this gives researchers more freedom to explore multiple codes—and more opportunities to reduce the qubit and time overheads of fault tolerance.

Building on an architectural advantage

The Jülich study provides independent evidence that Quantinuum’s architectural choices deliver advantages for operations essential to fault tolerance. It also reveals performance and implementation gaps in the superconducting systems tested.

We intend to extend that lead. Our investments in fidelity, flexible connectivity, and integrated control are foundations for increasingly capable machines.

As error-correction workloads become more demanding, those capabilities become more consequential. Quantinuum is building to meet that challenge—and to keep raising the performance bar.

Thirty participants gathered at Newnham College, Cambridge, for an intensive five-day programme exploring how quantum computers can be used to model chemical systems.

By: Duncan Gowland, Senior Advanced R&D Scientist at Quantinuum

For 30 researchers who gathered at Newnham College, Cambridge this September, that question shaped an intensive five-day programme.

Quantinuum’s first InQuanto Summer School, supported by the UK’s National Quantum Computing Centre (NQCC), combined lectures, hands-on workshops, and mini-projects to help participants connect fundamental concepts with practical research. By the end of the week, participants were applying those ideas to problems ranging from molecular electronic structure to quantum state-preparation circuits.

Applying quantum computing to chemistry can be challenging because it draws on expertise from a broad range of areas, including electronic structure theory, quantum algorithms, software, hardware, noise modelling, and resource estimation. Researchers often enter the field with deep knowledge of one or two of these areas but less familiarity with the others.

A key objective of the course was to help researchers from computational chemistry and quantum computing build stronger foundations, develop a shared language, and deepen their understanding of adjacent disciplines.

For researchers beginning work at this intersection, that shared understanding can make it easier to identify where to start, which assumptions to challenge, and when to seek expertise from another discipline.

Bringing Theory and Practice Together

The programme began with lectures on the foundations of quantum computing, followed by pen-and-paper and computational exercises in the afternoon. On Day 2, lectures and workshops introduced the electronic structure problem: how quantum chemists describe the behaviour of electrons in molecules.

Students then brought these two foundations together by studying fermion-to-qubit mappings, which translate chemistry problems into a form that quantum computers can process, before moving on to the measurement of chemistry observables and the construction of quantum algorithms.

Learners were given free access to InQuanto, Quantinuum’s quantum chemistry software platform designed to accelerate research in fields such as chemistry and condensed matter physics using quantum computers. InQuanto supported the lectures as a broad, well-documented, and thoroughly tested platform that enabled students to explore and reinforce complex concepts through hands-on experience.

Later in the week, attention shifted to state-of-the-art considerations: how to build useful chemical models, make the best use of current quantum devices, and think about how quantum algorithms for chemistry might develop over the next five to ten years.

The students, who travelled from around the world to Cambridge, brought a remarkable range of backgrounds and experience. The material was pitched at roughly first-year doctoral level, for participants with at least a year of experience in one core area and some Python skills. Even with this experience, participants benefited from exposure to other disciplines and from the practical expertise of Quantinuum’s quantum chemistry team—which brings years of experience working with industrial including BMW Group, TotalEnergies, NVIDIA, and Pfizer.

Throughout the week, participants were highly engaged in lectures and brought a thoughtful, collaborative approach to the workshop exercises. Discussions continued throughout the week, from coffee breaks to lunches and dinners, creating valuable opportunities to exchange ideas and experiences.

A particular highlight was the mini-project work. In just a day and a half, participants tackled a wide range of problems and produced impressive prototype solutions. The projects concluded with a poster-style session, where the quality of discussion was exceptional. Examples included state-preparation programmes using mid-circuit measurement in Guppy; investigations of active-space selection and quantum-selected configuration interaction (QSCI) for the chromium dimer; and the use of the atomic valence active space (AVAS) approach to identify active spaces and perform resource estimation for myoglobin.

One participant reimplemented their research on efficient state-preparation circuits based on orbital entanglement in InQuanto and compared the performance of those circuits with the platform’s efficient ansatz methods on the Helios emulator. It was rewarding to see participants apply these tools to their own research challenges and explore new approaches to quantum chemistry.

With Support from the UK’s National Quantum Computing Centre

This rewarding week was made possible through the support of the UK’s National Quantum Computing Centre, whose partnership helped bring the programme to life, and by the contributions of colleagues across Quantinuum. The teaching team brought together experts from our quantum chemistry, compiler, and error-correction teams, who developed the course materials, delivered lectures, and led the workshops.

Above all, the programme demonstrated the value of bringing researchers together around a shared foundation, practical tools, and opportunities to learn from one another. We look forward to seeing how participants build on these ideas in their future research.

Interested in future training opportunities, workshops, and community events from Quantinuum? Join QNET to stay connected with the latest updates, resources, and opportunities to engage with the quantum computing community.

For years, the quantum landscape has been crowded with headline hype, speculative roadmaps, and vanity qubit counts.

While that noise hasn't completely disappeared, Quantum World Congress 2026 proved that the market's tolerance for hype is rapidly wearing thin. While benchmark simulations and roadmap announcements still generate headlines, the real conversation on the ground has shifted toward a far more demanding question: who is actually delivering useful execution today and making meaningful progress toward fault-tolerant quantum computing?

At Quantinuum, our stance has never wavered. Progress is measured by delivering integrated systems that perform useful computation reliably on real hardware and in live enterprise environments. Prove what you can do and publish the results.

What was most encouraging this year at Quantum World Congress was seeing much of the industry increasingly align around that view, with the conversation converging around several priorities in particular.

1. Fault tolerance proven on real hardware is the only credible path to scale
The industry is converging around a shared reality: useful quantum computing will be defined by fault tolerance.

But fault tolerance is not a single metric, a logical-qubit count, or a standalone error-correction demonstration. It requires a complete end-to-end architecture that can detect, correct, and manage errors across every layer of computation, from logical operations to resource-state preparation, and execution of useful algorithms on live hardware.

That is why context matters when evaluating industry announcements.

Demonstrating more logical qubits is not the same as demonstrating fault tolerance. Demonstrating real-time decoding in a simulation is not the same as demonstrating real-time quantum error correction on real hardware. Logical qubits, decoders, syndrome extraction, and resource-state preparation are all necessary ingredients. But proving an individual ingredient in isolation, particularly through simulations or benchmark environments, is fundamentally different from proving that a complete fault-tolerant architecture works on a live quantum computer.

The industry should not confuse progress on individual building blocks with proof of a scalable fault-tolerant system. The real test is whether all these capabilities work together to improve computational performance beyond the physical layer and in a way that can scale.

This is exactly what Quantinuum's Helix architecture is designed to do.

Running on our Helios system, Helix integrates the full stack required for fault-tolerant quantum computing and has already delivered world-class results on live commercial hardware. Helios achieves physical two-qubit gate error rates of 8×10⁻⁴, while Helix has demonstrated a logical compute error rate of 2.8×10⁻⁴, representing a 4.28× improvement over the physical Clifford gate error rate without post-selection, meaning no cherry-picked results.
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Just as importantly, Helix improves efficiency as well as reliability. Through adaptive syndrome extraction, it reduces physical gate requirements by 33% and wall-clock execution time by 23%. Today, the architecture is already supporting full computations using 64 error-detected logical qubits with better-than-physical performance and a highly efficient 1:1.5 encoding rate.

These results matter because they demonstrate more than isolated milestones. They show that a scalable fault-tolerant architecture is operating on real hardware today and delivering measurable improvements in computational reliability. As physical systems grow, Helix provides the framework that enables additional scale to translate into increasingly reliable computation.

The path to fault tolerance will continue to evolve. New codes, encoding approaches, and implementation techniques will emerge over time. What remains constant is the need for an architecture capable of orchestrating those innovations into a practical, scalable system. We believe Helix is well positioned to serve as that architecture.

2. The industry must measure performance with useful, standardized metrics
Customers cannot make informed buying decisions if every vendor continues to grade their own homework. Standardized benchmarks are critical because they shift the conversation from theoretical promises to actual, measurable usefulness.

While earlier benchmarks like Quantum Volume were helpful for NISQ-era systems, they don’t keep pace as fault-tolerant systems scale beyond classical simulation limits. That is why Quantinuum is helping drive industry alignment around QUOPS, developed by Sandia National Laboratories with input from NVIDIA and Quantinuum.

QUOPS measures true computational capability by evaluating both computational Size (QUOPS) and Speed (QUOPS/sec). It’s the difference between rating an engine by theoretical horsepower versus measuring how fast and far a vehicle can drive on an actual track.

Most importantly, QUOPS evaluates the capability delivered by the complete computing stack. As the industry moves toward fault-tolerant systems, useful benchmarks must measure the performance of the entire architecture rather than individual subsystems. They must remain transparent as workloads scale, allowing enterprises to clearly evaluate the real-world utility of a system for their specific problem sets.

3. The industrial manufacturing race is on
Proving a fault-tolerant concept on a laboratory bench is only the first step. The transition to utility-scale computing is fundamentally an engineering, supply chain, and manufacturing race.

Our roadmap clearly defines how we scale from a single chip with a 2D-grid layout to larger multi-chip packages. Rather than reinventing the wheel, we are leveraging proven semiconductor manufacturing techniques that built the modern microelectronics industry. We have backed this architecture with a world-class industrial ecosystem, partnering with commercial foundries and component leaders.

The companies that win the next phase of quantum computing will not simply invent breakthrough technologies. They will demonstrate the ability to manufacture, deploy, and scale them reliably.

4. Growing the ecosystem requires a powerful developer platform built for the fault-tolerant era
A high-performing QPU is only part of the equation. You also need the software layer required to run it. For quantum to succeed, it must integrate into the workflows developers and enterprise researchers are already using today.

Nexus is our developer platform designed to be the purpose-built enterprise integration layer, designed from the ground up to squeeze maximum power and accuracy out of Quantinuum’s architectures. Nexus provides seamless interoperability across native Guppy, NVIDIA CUDA-Q, and Microsoft Q#, directly connecting quantum hardware to classical AI and HPC environments.

This is not an experimental platform. It is already supporting meaningful commercial adoption today.

Adopted by more than 200 organizations, representing approximately 45% growth, and used by more than 1,000 active developers, Nexus has seen annual job submissions increase 10x when comparing August 2024-August 2025 with August 2025-August 2026.

As quantum computing enters the fault-tolerant era, the winning platform will be the one that makes advanced quantum capabilities accessible within the workflows enterprises already depend on.

5. Real progress is measured in live production deployments and real-world research
The future of high-performance computing isn't quantum versus AI or HPC. It is all three working together as a unified compute stack.

But we believe the true test of market leadership isn't a self-funded trial or a desktop simulation that claims hybrid integration. It is putting live hardware into mission-critical operational environments to solve real enterprise problems and advance meaningful science.

That is why we are focused on embedding our quantum systems directly into global computing infrastructure. Through our partnership with Oracle, we expect to bring commercial-scale quantum computing directly into an Oracle Cloud Infrastructure AI data center, with the goal of letting enterprise customers run quantum applications within their existing cloud footprint.

Similarly, our deep, multi-year collaboration with NVIDIA continues to advance the frontiers of hybrid compute: from co-founding the NVAQC center in Boston to physically integrating NVIDIA GPUs into our Helios hardware for real-time error correction, to running live workflows with Pfizer to help accelerate pharmaceutical discovery.

Ultimately, as we scale these capabilities from the system level to the enterprise, we believe quantum computing can serve as the crucial engine to unlock far greater value from classical AI. By integrating QPUs alongside GPUs and supercomputers, we are working to move beyond the limitations of classical compute alone.

This hybrid approach enables enterprises to generate higher-quality quantum training data, build more powerful models, and tackle complex chemistry and materials challenges that remain beyond the reach of classical AI by itself.

Looking Ahead‍

Quantum World Congress 2026 proved that the market is finally asking the right questions:

• How do we achieve true fault tolerance at scale?
• How do we measure objective computational capability?
• How do we deploy quantum systems to solve real-world enterprise problems?

The conversation is shifting from theoretical possibility to demonstrated capability. From isolated milestones to integrated systems. From simulations to real hardware. That is where meaningful progress happens. And that is where Quantinuum intends to continue to lead.

Forward-Looking Statements:

This blog post contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995, including statements about expected product capabilities, technology development timelines, planned partnerships and deployments, and anticipated market trends, and expected business metrics and growth rates. These statements are based on current expectations and assumptions and are subject to risks and uncertainties that could cause actual results to differ materially, including risks related to technology development, competitive dynamics, customer adoption, and partnership execution. Forward-looking statements speak only as of the date made, and Quantinuum undertakes no obligation to update them. For a discussion of factors that could affect outcomes, please refer to Quantinuum's public filings.