• 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.

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.