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.

For enterprise and public sector executives evaluating quantum computing investment

Quantum computing is now a strategic priority for many organizations. It's on track to help solve some of the world's biggest challenges, from drug discovery, to materials science, to optimization problems – all at a scale classical computers simply can't reach. For executives responsible for R&D, technology strategy, or innovation investment, the question is no longer whether quantum computing matters. It's how to approach it wisely.

That's a harder question than it sounds. The quantum computing market is crowded, technical, and moving fast, and most of the guidance available is written for physicists, not for the executives who actually have to make the investment decision. Vendor claims are difficult to compare, pilot programs are easy to get wrong, and the gap between "quantum is exciting" and "quantum is worth investing in this year" isn't always well explained.

Our new guide, A Strategic Guide to Selecting the Right Quantum Computing Solution, is built to close that gap.

What's Inside

The guide is designed to give business and technology leaders a clear, practical path through four essential questions:

• Why quantum computing matters now, and why the window for early strategic advantage is open today
• How to evaluate vendors objectively, using a structured framework rather than marketing claims
• How to design an effective pilot, so early investment produces real, usable evidence
• How to take your first steps with confidence, whether you're just starting to explore or ready to scale

It also includes a glossary of key terms, so readers new to the field aren't left decoding jargon before they can evaluate a single vendor.

Who Should Read It

The guide is written for CTOs, CIOs, CISOs, R&D leaders, and program directors across enterprise and public sector organizations, at any stage of quantum familiarity. Whether your organization hasn't yet started exploring quantum computing, or you already have a program underway and are looking to sharpen your evaluation process, the framework inside is designed to apply.

How to Use It

The evaluation framework at the core of the guide isn't specific to any one vendor; it's designed to be applied to any quantum computing solution you're considering, so you can make an apples-to-apples comparison based on your organization's actual needs. The guide also walks through how Quantinuum maps to that same framework, and what it looks like to work with Quantinuum as a co-development partner, should you want a concrete reference point alongside the general framework.

Start With Confidence, Not Guesswork

Quantum computing is a strategic decision, not just a technical one. The organizations that approach it with a clear framework, rather than reacting to the noise, will be the ones positioned to capture real value as the technology matures.

• QUOPS is a new, architecture-agnostic benchmark designed to measure quantum performance across physical- and logical-qubit systems, using two metrics: Q (computation size) and Ω (operations per second).
• It addresses the limits of traditional metrics like qubit count, gate fidelity, and gate speed by measuring what a quantum system can actually execute successfully—including the effects of error correction, decoding, mitigation, compilation, and other system-level factors.
• QUOPS aims to create a common language for the industry, helping vendors demonstrate progress, enabling buyers and governments to make more objective procurement decisions, and giving researchers a standardized way to track progress toward utility-scale, fault-tolerant quantum computing.

Quantum computing is entering a new era. As systems move from Noisy Intermediate-Scale Quantum (NISQ) toward Fault-Tolerant Application-Scale Quantum (FASQ), traditional metrics like qubit count, gate fidelity, and gate speed are no longer enough to describe what a machine can actually deliver.

Introducing QUOPS

Developed by Sandia National Laboratories, with input from Quantinuum and NVIDIA, QUOPS—the Quantum Universal Operations Performance System—is a common, architecture-agnostic benchmark for measuring quantum performance across both physical- and logical-qubit systems on the path toward quantum utility.

QUOPS can be applied to different architectures, codes, modalities, and levels of fault tolerance. QUOPS runs the same randomized workloads across different computational shapes, measures whether each workload succeeds, identifies the boundary of a system’s capability region, and reports two summary metrics:

• Q: the largest benchmark circuit size that passes the success threshold inside a utility-motivated region. Size is defined as 2*(width)*(depth).
• Ω: the effective operations per second at that point.

The result is a direct measure of how much computation a system can perform and how quickly it can do so. Together, these measurements provide a two-dimensional view of capability while reducing system performance to a common currency: quantum operations.

Why Quantum Computing Needs a New Metric

Component-level metrics remain essential for engineering. Qubit count, two-qubit fidelity, and gate speed can reveal control errors, crosstalk, leakage, connectivity constraints, and other system limitations. But they do not necessarily predict system-level performance.

Fault tolerance makes this gap even larger. Physical operations become logical computation with the addition of logical encoding, syndrome measurement, decoding, logical gate construction, magic-state production, routing, and control. Ultimately, this means that fault tolerance expands the relevant currencies of computation. Code distance, logical fidelity, magic-state throughput, decoding, connectivity, and space-time volume can matter far more for performance than raw qubit count or individual gate speeds.

This creates a growing challenge for buyers, governments, and researchers. As organizations move from experimentation toward larger-scale and potentially on-premise quantum systems, they need to know a simple thing:

What computation can a machine actually execute successfully?

QUOPS addresses that question by measuring the integrated system rather than inferring performance from individual components.

This is particularly important as the field considers workloads requiring roughly 10⁹–10¹² operations on thousands of qubits. Today's measured capabilities are still orders of magnitude smaller; QUOPS turns that gap into a measurable quantity.

QUOPS for Procurement

QUOPS can also provide a practical layer for quantum procurement and planning.

HPC centers need to understand when quantum computing will become useful for real workloads. Customers may have a goal of procuring a system that can, for example, run a trillion error-free operations. Today, answering these questions can require complex resource estimates that depend on hardware modality, QEC code, magic-state factories, decoding, compilation, and other architectural choices.

In both cases, QUOPS provides a simpler system-level reference point: Q describes the size of computation a machine can execute, while Ω describes its effective throughput. Furthermore, because QUOPS is architecture-neutral and includes anti-gaming provisions, it can also help buyers compare competing systems without relying solely on vendor-selected metrics or announcements.

How Quantinuum stacks up

While QUOPS is a new benchmark, it has already been measured on several vendors’ hardware. This marks an important step for our industry: we can now compare vendors directly, assessing their capabilities in a way that flattens the differences introduced by modality and architecture choices.

Figure 1. The QUOPS capability region and score for state-of-the-art processors from Quantinuum, Google, and IBM (adapted from Figure 2 of the scientific publication co-authored by Quantinuum, Sandia National Laboratories, and NVIDIA). QUOPS specifies a random circuit construction that can be built for a specified width (number of qubits) and size (number of quantum gates). A set of circuits is run at several width and size points and the average fidelity of those circuits are measured and compared to a predefined threshold. Each labeled point above represents experimental data from QUOPS circuits that passed the threshold with high confidence. The lines are filled capability limits of each machine between the points. The stars indicate the QUOPS score (Q), which is the experimental data point that passes the threshold with maximum size inside the shaded cone of width² ≤ size ≤ width³.

‍Figure 2. The QUOPS score (Q) vs rate (Ω) for state-of-the-art processors from Quantinuum, Google, and IBM (adapted from Figure 2 of the QUOPS scientific publication co-authored by Quantinuum, Sandia National Laboratories, and NVIDIA). Each point is the maximum QUOPS circuit size that passes the threshold within the specified cone and rate that it was run. The dashed lines indicate the extrapolated effect of error mitigation, which attenuates the rate by including the shot overhead needed for general-purpose error mitigation. The gradient lines show the estimated runtime of a circuit at a given score and rate.

Figures 1 and 2 show how QUOPS quantifies the capability tradeoffs between different systems. Willow and Boston are superconducting systems with very fast gate speeds but limited connectivity, while Helios is a trapped-ion QCCD system with effective all-to-all connectivity but much slower gates. Willow and Boston have smaller capability regions and QUOPS scores but higher QUOPS rates; while Helios reaches larger capability regions and QUOPS scores but lower QUOPS rates. All three systems have the ability to trade speed for larger circuits with error mitigation. This is commonly assumed in the community but is nicely quantified with the QUOPS rate, which accounts for the corresponding sampling overheads of general error mitigation techniques (as shown by the dashed lines in Figure 2).

Building a New Benchmarking Ecosystem

QUOPS will not replace every quantum benchmark. The field will continue to need application-specific suites, component-level measurements, hybrid-HPC benchmarks, and independent verification.

QUOPS instead serves as a common system-level yardstick that can make roadmaps more comparable, procurement more objective, and progress easier to track.

We are calling on vendors to report QUOPS metrics (Q, Ω) and capability regions alongside existing metrics, buyers and agencies to consider QUOPS thresholds in RFPs, and researchers to contribute fault-tolerant architectures and resource estimates.

As quantum computers become fault tolerant, success will no longer be defined simply by how many qubits a machine contains or how low its error rates are.

It will be defined by the computation the machine can deliver.

QUOPS is a step toward measuring that capability—and toward giving the quantum industry a benchmark built for the era ahead.

• A new experiment tests what makes a quantum computer genuinely quantum by using a simple game that demonstrates a provable advantage from quantum superposition, without relying on entanglement or assumptions about classical computational difficulty.
• The test was demonstrated on Quantinuum’s System Model H2, where researchers ran thousands of circuits and observed results close to theoretical quantum predictions. The approach also offers an efficiently verifiable way to test for non-classical behavior.
• The work provides a new way to think about quantum-computing verification: rather than focusing only on metrics like qubit count, we can ask whether a machine demonstrates capabilities that fundamentally distinguish quantum systems from classical ones. This validates that the computer is working as intended.

Building a quantum computer is one thing. Showing that it is genuinely using quantum mechanics is another.

A new experiment, just published in Nature Communications, takes a fresh approach to that question. Instead of relying on entanglement or the complex calculations often used to benchmark quantum computers, researchers designed a simple game (initially published in Physical Review Letters) that tests something more fundamental: quantum superposition.

Using superposition, the team constructed a game where quantum mechanics provides a provable advantage over classical approaches. Once the game was set, the team ran it on real hardware. The results showed a clear performance gap between the best possible classical system and our System Model H2 – a gap that only grew as the test became more difficult.

A game that classical computers can’t win

The game is played by a single player with access to a computer. The player receives a quantum state representing a set of numbers—for example, {0, 1, 5, 7}. Their goal is to return a number that belongs to the complement of that set: {2, 3, 4, 6}.

That sounds simple. But as the size of the sets grows, something remarkable happens.

A classical strategy needs to test many numbers to succeed. A quantum strategy, however, succeeds in one step. The authors show that the quantum strategy has a score that grows exponentially faster.

Importantly, this isn't based on an assumption that this problem is difficult for classical computers. The separation is mathematically proven. In other words, the researchers can show that the quantum advantage exists without relying on unproven assumptions from complexity theory.

Using our System Model H2, the experimenters were able to confirm the theoretically derived separation between the quantum and the classical strategy (up to the largest sizes they could fit on the quantum processor) with high confidence – showing that the violation remained close to exponential.

Testing quantum mechanics without entanglement

Many famous experiments testing quantum behavior rely on entanglement and non-locality, where multiple parties share parts of a quantum system.

This experiment is different.

There is only one player, who has access to the entire quantum system. The advantage comes from superposition—the ability of a quantum system to exist in a combination of states until it is measured.

That distinction matters because it provides another way to ask whether a quantum computer is actually behaving quantum mechanically.

The researchers turned their game into an experimental test and ran thousands of different circuits on Quantinuum's System Model H2. The scores they observed were close to the theoretical predictions for a quantum strategy.

Why verification matters

One of the challenges with existing quantum-computing demonstrations is figuring out whether the machine really produced the result it was supposed to produce.

For example, random circuit sampling can be extremely difficult to verify classically as systems become larger. That creates a tension: you want to demonstrate that a quantum computer is doing something a classical computer cannot easily reproduce, but you also need a practical way to check the result.

The complement-sampling game offers a different approach. The violation of classical performance can be efficiently verified with a classical computer.

That makes the test potentially more scalable: you don't need to reproduce the entire quantum computation on a classical computer just to determine whether the machine demonstrated non-classical behavior.

So, what makes a quantum computer “quantum”?

The deeper message of the experiment is that demonstrating a quantum computer isn't simply about having qubits.

A convincing demonstration should show that the machine is exploiting properties that genuinely distinguish quantum computation from classical computation. Here, the researchers focus on one of those defining properties—superposition—and construct a game where quantum mechanics provides a provable advantage.

This first experimental demonstration of complement sampling doesn't close every possible loophole, which is common for this sort of experiment – closing the major experimental loopholes in Bell-inequality tests took decades—a body of work that ultimately contributed to the 2022 Nobel Prize in Physics. The researchers explicitly note that the implementation relies on assumptions about how the input state is prepared, so the experimental results should be interpreted with some caution.

Still, the work provides a new way to probe the boundary between classical and quantum computation.

And that may be the most interesting part: rather than asking only “How many qubits does the machine have?”, we can ask a more meaningful question—

“What can this machine do that only a quantum system can?”

That is ultimately what it takes for a quantum computer to actually be quantum.

• Helix is Quantinuum’s QEC architecture designed for scalability, leveraging reconfigurable connectivity to reduce the physical-qubit and time overhead required for fault-tolerant computation.
• The architecture has been experimentally validated on Helios, demonstrating logical memory, logical computation, and logical entanglement across different QEC codes—all outperforming corresponding physical-level results without post-selection.
• Helix provides a foundation for Apollo, combining efficient logical gates, multiple QEC encodings, and a path to universal fault-tolerant computation. The results on commercial hardware validate Quantinuum’s roadmap toward scalable, fault-tolerant quantum computing.

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.

But there’s still a way to go. One of the defining challenges over the coming years will be putting it all together into a usable – and scalable – fault tolerant architecture. Today, we are excited to announce that we have experimentally validated one of our own leading candidates for such an architecture, the Helix code.

With this demonstration, we have put all the pieces together: logical memory, logical computation, a heterogenous code architecture that optimizes for magic vs gates, all with super efficient operations and record-breaking² fidelity.

The Challenge of Fault Tolerance

The delicate nature of qubits gives them their strength – they can be entangled, placed into superpositions, and even teleported. However, this comes at a cost: on the hardware level, quantum bits (qubits) will always be noisier than classical bits.

Enter quantum error correction (QEC). QEC moves us past prohibitive physical noise to fidelities that really matter; where industrial workflows and scientific discovery live. Our field has been hard at work to realize - and optimize - QEC, and we are finally starting to reap the fruits of that labor.

However, for the most part, this work has taken shape only a few pieces at a time: a demonstration of fault tolerant gates here or memory there, sometimes even a full fault-tolerant algorithm, but rarely do we see demonstrations at the architectural scale needed to build our next generation of machines.

The difficulty is that encoding and performing fault-tolerant computation costs considerable space (qubit number) and time (circuit complexity), which QEC researchers summarize with a “spacetime volume”.

‍Introducing Helix: A QEC Architecture for Apollo

The Helix code was custom-designed to usher in the next generation of fault tolerance.

Using our reconfigurable qubits, we designed Helix to minimize its spacetime volume by employing more exotic entanglement schemes compared to traditional codes (imagine cat’s cradle compared to a simple, 2D net). This entanglement complexity is impossible with processors that don’t have reconfigurable connectivity.

Ultimately, this translates to a code that requires fewer physical qubits per logical qubit, while also giving you fast and simple computing.  

In general, gates between logical qubits can be quite difficult because logical qubits are composed of physical qubits that are entangled together in some specific way. Sometimes, a single physical qubit may even be shared between multiple logical qubits, as is the case with codes that offer lots of logical qubits per physical qubit. Performing gates across these complex structures can be tricky, and can take a lot of individual operations on physical qubit pairs to accomplish.

There are two major exceptions. The first, called a transversal gate, is where the logical operation maps directly onto the physical one: you just perform a regular 2-qubit gate between each physical qubit in each logical qubit.

The second is simpler still: gates can be accomplished by simple software-level qubit relabeling (eg simply renaming qubit A to qubit B), combined with easy, single qubit gates. This type of automorphism, or permutation-based gate, is particularly elegant.

The Helix code makes heavy use of transversal and automorphism gates, making it considerably faster and easier to compute with than a lot of other options. Ultimately, this translates to a significant reduction in both space (qubit) and time (circuit complexity) overheads: less space is needed for block encoding and ancilla; and time is drastically reduced when simple software relabeling or transversal gates are performed in the place of expensive protocols like lattice surgery.

Demonstrating the Building Blocks of a Fault-Tolerant System

Experiment 1: Logical Memory

The team started by showing that the Helix code can successfully preserve encoded quantum information for extended periods of time.

To show this, the team started with their logical qubits in a given state. Then, they performed 20 rounds of syndrome extraction, paying special attention to leakage (a dominant source of error on Helios). To remove leakage, the team leveraged Helios’ new leakage repump capacity, as well as circuit-level leakage reduction units.

Result: per qubit, per round, they achieved an error rate of 4.6 x 10^-5, with no post selection.

This amounts to a block logical error per round of 9.3 x 10^-5, with no post selection. With a small amount of post selection (0.5%), the block logical error per round was reduced to 1.9 x 10^-5.

Why This Matters

Quantum memory is one of the most fundamental building blocks of a fault-tolerant quantum computer. A useful quantum processor must be able to preserve quantum information long enough to perform the computation, error correction, and communication required by larger algorithms.

These results prove that encoded quantum information can be preserved with a lower error rate than the underlying physical operations – all without needing post selection.

Experiment 2: Logical Computation

A central feature of the Helix logical architecture is that encoding multiple logical qubits does not require correspondingly expensive logical computation. By construction, this code has a variety of logical gates all implementable with only physical single-qubit gates and qubit relabeling. These ‘SWAP-transversal’, or ‘automorphism’, gates provide the ability to do some logical circuits essentially for free, as permutations are realized by simple ion-transport and software level relabeling.

The team experimentally tested the code’s computational abilities by benchmarking the complete logical Clifford group (i.e., all gates except for T gates) while interleaving up to 27 rounds of active adaptive syndrome extraction.

Result:  2.8 x 10^-4 logical error rate per Clifford gate, a significant improvement (4.28x) over Helios’ physical 2-qubit Clifford error rate, again achieved without post selection.

This impressive result is partially enabled by the team’s clever adaptive syndrome extraction (ASE) technique. Their ASE technique reduces the number of physical gates required per logical gate by about 33%. This pruning also shortens the physical run time, reducing the ‘wall clock duration’ by about 23%. Both gates and idling contribute significantly to errors, so these reductions translate to a lower logical error rate.

Why This Matters

This experiment proves the Helix code’s ability to compute, all while showing significant improvement over the physical level with no post selection. In addition, this marks the first demonstration of randomized benchmarking on a code encoding more than one logical qubit, an important milestone for our community.

Experiment 3: Universality via Logical Entanglement Across Different Codes

Clifford gates alone are insufficient for universal fault-tolerant computation; our QEC architecture must also provide access to non-Clifford resource states (often called “magic”). While the Helix code has many desirable features in terms of Clifford gates, it’s sub-optimal for preparing magic states. Rather than forcing the Helix code to work in this regime, we developed our architecture to employ two codes; one for Clifford gates and memory, and one for magic state preparation. Using different codes each optimized for their own tasks, called a heterogenous architecture, makes the entire assembly considerably more efficient and cost effective.

The trick that makes it all possible is something called chain-mapping, that allows the QPU to smoothly switch between underlying encoding schemes. To test this, the team used a rotated surface code for magic state generation, which would then be injected into the computational (Helix) code to generate non-Clifford gates (enabling fault tolerant universal computation).

Rather than performing magic state injection directly, the team wanted to benchmark the interface (the chain-map). To do this, they used their chain-mapped gates to prepare a three logical qubit GHZ state that spans the two different codes. The resulting GHZ state contained 1 logical qubit from the surface code and two from the Helix code, making for a truly heterogeneous structure.

Result: The logical GHZ state had a fidelity lower bound of 99.925%, and an upper bound of 99.975%. The lower bound exceeded the physical baseline, making all three experiments better than their physical counterparts.

Why This Matters

This experiment demonstrates one of our key architectural advantages: using our reconfigurable connectivity to employ multiple QEC encodings in a single fault tolerant architecture, improving our efficiency and reducing qubit costs.

Accelerating the Path to Apollo

The quantum computing industry has proposed many approaches to error correction. This paper marks one of the first experimentally validated plans for a QEC architecture. With the low logical error rates (all improving on the physical baseline), the practical logical operations (which drastically reduce qubit costs and compute time), and real commercial hardware performance, this result helps to prove that we will deliver on our roadmap.

A crucial element of this demonstration is that these results were obtained on our commercial hardware. This is not a result from a testbed, or a result from hardware that has limited functionality. This is a result from the same computer as our customers use for their own research.

Furthermore, simulations indicate that the improvements in physical fidelity we expect from moving from Helios to Apollo will bring logical error rates in line with our roadmap targets. Because logical error rates depend strongly on physical error rates, Apollo’s expected improvements at the physical level should translate directly into lower logical error rates. Importantly, we expect to achieve these gains without increasing the code distance or using additional physical qubits per logical qubit.

Building a fault-tolerant quantum computer requires solving multiple engineering challenges. In this result we have proven our path to a scalable QEC architecture, showing not just one-off results on test stand hardware, but a harmonious whole consisting of:

✓ A candidate architectural code

✓ Logical memory

✓ Logical computation

✓ A path to magic

✓ Multiple encodings in one architecture

✓ Commercial hardware compatibility

By validating our fault-tolerant architecture on real commercial hardware, Quantinuum has taken a significant step toward Apollo - and toward quantum computers capable of solving meaningful problems at scale.

¹ Noisy Intermediate-Scale Quantum

² Based on a study of existing literature