Accuracy Is the Foundation of Meaningful Quantum Computing
Recently, industry peers—including Quantinuum’s Startup Program Partners Qedma and BlueQubit, as well as our partner RIKEN—published a paper exploring quantum magnetism that “extends beyond the reach of the state-of-the-art classical methods considered;” evidence of a quantum advantage result. Interestingly, the team validated their results on Quantinuum machines (both Helios and System Model H2).
In 2025, we published a paper (now in Nature), exploring a similar system - also at scales that frustrate classical computation. This got us thinking: with more successes like this in the literature, what does this mean for the ecosystem at large? What are the key lessons to learn from these early demonstrations? And, perhaps most importantly, what’s next?
We Are Entering the Era of ‘What’, Not ‘When’
The answer to the first question, ‘what does this mean for the ecosystem at large’, is a delight to answer. After decades of promises, we are finally in the era where quantum computing is matched with, if not outright exceeding, classical HPC and supercomputing.
Examples of (complexity-theory proven) quantum advantage are already common, usually in the form of Random Circuit Sampling. This was extended to generating certified randomness, which was one of the earliest commercial applications of quantum computing.
Since then, we have seen a number of results from different groups that push the limits of classical computing while exploring ‘real’ problems; these range from papers exploring quantum magnetism (as mentioned above), to papers exploring things like superconductivity or peaked circuits.
Whether or not these are definitively ‘quantum advantage’ results is almost beside the point. They mark a distinct place on the path towards broad scale quantum utility, when quantum computers will be widely useful for researchers and industry alike. More importantly, these papers all speak to a certain level of ‘technological readiness’, showing that quantum computers are now proven to work on problems that are relevant (to some people, at least), at scales that aren’t easily reproduced elsewhere.
Accuracy is a Baseline Requirement
One of the key lessons we can learn from all these demonstrations is that hardware accuracy is paramount. Without accuracy, quantum computers are very expensive noise generators, unable to move the needle beyond HPC. Happily, we are finding that current generation machines are still capable of quite a lot, thanks to baseline physical accuracy, optionally coupled with clever error mitigation on top.
In general, we are very pleased to see how error mitigation can significantly reduce the impact of hardware errors and improve the quality of computed results by applying sophisticated post-processing techniques. However, error mitigation is not free. As hardware noise increases, mitigation becomes increasingly computationally expensive, and the techniques themselves can skew the results. If the underlying hardware is insufficiently accurate, it becomes more difficult to distinguish genuine physical phenomena from artifacts introduced via mitigation.
This is precisely where hardware quality matters. The validation on Quantinuum systems provided an important independent confirmation that the mitigated results from another vendor reflected real physical behavior rather than bias introduced through the mitigation process. Because Quantinuum's hardware operates with substantially lower native error rates, it served as a high-confidence reference point for validating scientific results. Importantly, this validation was about confirming the underlying physics, not validating a claim of quantum advantage.
Hardware Accuracy Improves Computational Efficiency
All the above reflects our systems' strong native performance: when hardware begins with exceptionally high fidelity, there is simply less error to overcome. However, error mitigation remains an interesting and valuable approach: high-quality hardware establishes the baseline, and software extends what is possible.
However, native accuracy affects more than scientific confidence—it also influences computational efficiency. As program complexity and size increases, hardware error rates increase, and successful error mitigation generally requires more sampling, more processing, and more computational resources. The lower the physical fidelity, the greater the overhead required before arriving at trustworthy results.
By starting with significantly lower native error rates, Quantinuum systems reduce the amount of mitigation needed to achieve comparable scientific outcomes. This creates a practical advantage in computational cost while helping to preserve confidence in the resulting data.
Accuracy is the Foundation for Fault Tolerance
Finally, we can answer the question of what comes next. This may seem obvious, but it’s multifaceted. What’s next is large-scale fault tolerant quantum computing. But the real question is, what does that look like?
A truly large-scale fault tolerant quantum computer will operate with error correction embedded into the workflow, working on the ‘logical’ level. That means that programmers will write their code to operate on logical qubits, with all the mechanisms of error correction hidden under the hood. The result will be error rates low enough to run some truly behemoth workflows.
We are well along the path to realizing this at scale: we have demonstrated all the necessary primitives, have world-leading logical error rates, and have a platform flexible enough to use new codes as they are invented; a crucial advantage in a quickly-evolving landscape. All of this is enabled by our high native accuracy; the accomplishments listed would be impossible without hardware that wasn’t ultra-low error to begin with.
However, even with the full force of error correction, error mitigation may still play an important role in the post fault-tolerance era. While error correction will be applied broadly to all workflows, there will still be some special cases where error mitigation may stretch the hardware further, always ensuring we stay on our front foot as computational power grows.
Progress Is the Real Milestone
Scientific breakthroughs matter because they move the field forward. But lasting enterprise value will come from quantum computers that consistently deliver results organizations can trust.
With Helios—the world's most accurate commercial quantum computer[1]—and a growing ecosystem of partners building complementary technologies, Quantinuum is creating the accurate, scalable foundation needed to transform scientific achievements into practical quantum computing.
[1] Based on two qubit gate fidelity, as of December 31, 2025.
About Quantinuum
Quantinuum, the world’s largest integrated quantum company, pioneers powerful quantum computers and advanced software solutions. Quantinuum’s technology drives breakthroughs in materials discovery, cybersecurity, and next-gen quantum AI. With over 500 employees, including 370+ scientists and engineers, Quantinuum leads the quantum computing revolution across continents.
- Mitsui & Co. and Mitsubishi Electric demonstrated one of the world’s largest approximate Quantum Fourier Transforms (QFT) on Quantinuum Helios, scaling from prior records to 98 physical qubits.
- The collaboration also implemented a logical QFT using a QEC (Quantum Error Correction) code with up to 12 logical qubits.
- The work highlights Quantinuum’s accuracy and flexible architecture.
While there is ongoing debate around the pace of quantum computing’s development, a more grounded way to assess progress is through concrete demonstrations of foundational algorithms at meaningful scale. In this context, Mitsui & Co. and Mitsubishi Electric are taking a pragmatic view of quantum progress—focusing on how close the field is to executing core algorithmic primitives that underpin many potential industrial applications, rather than relying on abstract milestones or timelines.
In a new white paper, the industrial giants teamed up with Quantinuum to measure how close we are to running the Quantum Fourier Transform (QFT), a widely-used algorithmic primitive, at scales necessary for industrial applications. In the process, the team successfully ran one of the largest instances of the approximate QFT ever demonstrated. This achievement matters because the QFT is an essential primitive that underpins many of the quantum algorithms expected to deliver practical advantages.
You may have heard of the (classical) Fourier transform (FT), due to its ubiquity throughout modern computing. The FT is essential in everything from image analysis to data compression, with almost limitless applications in between. The quantum Fourier transform (QFT) is similar; it’s used in everything from chemistry to finance.
Because the QFT is a foundational primitive underpinning many quantum algorithms, demonstrating it at larger scales and higher fidelity is a practical way to measure quantum computing readiness. This is exactly the type of benchmarking that organizations should consider to understand where today’s systems are useful, and to see how fault-tolerant approaches are progressing. Ultimately, algorithm-level benchmarking like this is one of the most useful ways to understand not just where we are, but where we are going.
A Transformative Approach
Primitives like Fourier Transform are so widespread because they simplify problems by transforming them into something that is easier to deal with. At Quantinuum, we are very interested in transforms: not only are they crucial for industrial applications but they can also simplify algorithms, making them possible to run now instead of later. This ‘transformational’ approach extends beyond the QFT - other transforms exist, and we have even invented our own quantum-native transforms.
Using our Helios quantum computer and Guppy language, the joint team explored running the QFT on both physical qubits and on logical qubits, showing that fault tolerance is progressing quickly. Running the QFT on 98 physical qubits; the paper shows a clear progression from previous results.
Then, using the Steane code, one of the best-studied quantum error correcting codes, the team used Helios’ 98 physical qubits to form 12 logical qubits, successfully running the QFT with the mechanisms of quantum error correction interwoven into the algorithm. This marks a crucial step forward for the field.
Foundational Progress
Taken together, these results provide a more concrete lens through which to view progress in quantum computing: not as abstract projections, but as measurable advances in the execution of foundational algorithms at increasing scale. By benchmarking the Quantum Fourier Transform on both physical and logical qubits, Mitsui & Co. and Mitsubishi Electric are helping to clarify what today’s hardware can already achieve, and where fault-tolerant approaches begin to extend those limits.
More broadly, the organizations best positioned to benefit from quantum computing will be those that focus on these foundational capabilities early, and use them to build a clear, evidence-based understanding of how the technology fits into their business goals.
It is believed that unlocking answers to some of the most complex scientific and industrial problems will require the seamless integration of high-performance computing (HPC), generative AI (GenAI), and quantum computing. Toward this goal, Quantinuum, NVIDIA, and a major pharmaceutical company have successfully demonstrated the first step in a proof-of-principle framework designed to connect these three distinct computing paradigms for industrially relevant computational chemistry.
This milestone, enabled by three industry leaders and experts in their respective domains, serves as a foundational capability that could support the development of future hybrid quantum-AI workflows to help optimize industrial research and development (R&D).
The potential value is a path toward more automated, repeatable, and scalable workflows for translating chemistry problems into executable quantum programs—capabilities that could eventually make hybrid computing easier to deploy in industrial R&D.
The GenQAI Framework
The framework, termed Generative Quantum AI (GenQAI), involved a quantum computer simulating a pharmaceutical compound using programming instructions generated by an AI model, which itself was trained on quantum data that was simulated using HPC.
While the vision for GenQAI explores how future industrial simulation workflows might be optimized by training AI models using quantum data derived directly from a quantum computer, the framework currently consists of four main technical steps:
- Simulating quantum data: The process began by simulating quantum data with NVIDIA accelerated computing using NVIDIA CUDA-Q.
- Fine-tuning the AI: This simulated quantum data was used to fine-tune a pre-trained AI model from the open NVIDIA Nemotron family.
- Generating instructions: The AI model then generated quantum circuits, which are the programming instructions required for the quantum simulation.
- Validating accuracy: To validate the results, the circuits were run on Quantinuum’s Helios quantum computer using its InQuanto quantum chemistry platform.
The core novelty of this development lies within the process of the framework itself. In this proof-of-principle experiment, an AI model fine-tuned on simulated quantum data generated circuits that were successfully executed and validated on Quantinuum’s Helios system. Rather than delivering an immediate commercial advantage, this achievement establishes a credible, verifiable baseline for how HPC, AI, and quantum computing can function in tandem.
The Case Study
The validation of the framework represents an early step toward the goal of developing scalable architectures for the pharmaceutical industry.
With a shared view toward eventually scaling the framework for pharmaceutical R&D applications, the researchers simulated a pharmaceutical compound: imipramine. This anti-depressant was chosen because it serves as a model compound for drug degradation and shelf-life studies, which are standard components of the pharmaceutical R&D lifecycle.
Developing hybrid infrastructure that enterprises may adopt requires a deep, coordinated effort among domain experts. As such, this successful test highlights the value of combining the strengths of a quantum computing hardware and software leader (Quantinuum), with a hybrid-quantum classical platform (NVIDIA), and a leading enterprise end-user to build and test future computing capabilities for industrial chemistry.
Scaling the Framework
Although demonstrated on a pharmaceutical compound, the architecture could eventually inform similar molecular-simulation workflows in sectors such as energy, agriculture, advanced materials, and electronics. At this stage, it provides a reference for further testing and development.
Engage Further
Access the paper here to explore the full technical details of this demonstration and contact our team to learn more about joining Quantinuum’s enterprise partner network.
Quantinuum is pleased to announce that applications are now open for the Quantinuum SG Grand Challenge 2026, a global innovation challenge designed to bring together researchers, developers, scientists and innovators to explore practical applications of quantum computing.
Organized by Quantinuum and supported by Singapore's National Quantum Office and Aqora, the three-month program aims to foster collaboration across academia, industry and the quantum developer community while supporting the continued growth of Singapore's quantum ecosystem.
A Platform for Quantum Computing Innovation
Participants will work in teams to develop solutions across a range of challenge areas, including chemistry and molecular simulation, optimization, AI for quantum systems, quantum error correction, condensed matter and materials science, and open innovation. Throughout the program, participants will have access to mentoring, technical enablement and Quantinuum quantum computing resources.
Singapore Grand Finale
Selected finalist teams will be invited to present their work at the Grand Finale hosted in Singapore before representatives from academia, and industry. The event will celebrate innovative applications of quantum computing while providing an opportunity for participants to engage with Singapore's growing quantum community.
Join the Challenge
The Quantinuum SG Grand Challenge welcomes participants from around the world. Whether you are an experienced quantum researcher or beginning your quantum computing journey, the program offers an opportunity to collaborate, learn and contribute to the development of practical quantum applications.
Applications are now open. Spaces are limited and subject to review and approval.