Progressing your organization along the five levels of quantum maturity

Quantum computing has moved from a bet on the future to a race already underway. Early adopters are locking in strategic partnerships, building proprietary IP, and positioning themselves years ahead of competitors who are still watching from the sidelines. For executives, the question isn't whether to engage with quantum computing. It's how far along that journey your organization actually is, and what it takes to move forward.

That's a harder question to answer than it sounds. Quantum maturity isn't a single milestone you either hit or miss. It's a progression, built across talent, technology access, workflow integration, partnerships, and value realization, and most organizations aren't entirely sure where they currently stand and what to do next.

Our new paper, A Roadmap for Quantum Maturity, is built to answer exactly that.

A Framework for Where You Stand, and Where to Go Next

Drawing on extensive client experience, the paper lays out five distinct levels of quantum maturity, from early awareness through full transformation, along with the leadership actions that move an organization from one level to the next.

• Awareness — early conversations, but no clear ownership or use cases yet
• Exploration — exploring partnerships and prioritizing use cases with limited budget
• Experimentation — quantum roadmap established and guiding dedicated teams, funding, and partners to execute pilot use case projects
• Integration — quantum computing applications are being embedded into business unit workflows, and the quantum roadmap is integrated with broader digital technology strategy, including AI, HPC, and data
• Transformation — quantum capabilities are embedded in core products and decision-making, with differentiated, proprietary advantage

Most industry leaders today sit at the exploration or experimentation stages, with clear ambitions to reach transformation within the next several years. The paper breaks down what separates organizations that progress from those that stall out at proof-of-concept.

The Advantage Is Built, Not Bought

One of the paper's central takeaways is one many executives underestimate: investing in quantum technology alone isn't enough. Organizations that advance fastest pair that investment with a deliberate strategy, building quantum literacy across leadership and technical teams, honestly assessing capability gaps, focusing on a small number of high-impact use cases tied to real business metrics, and defining a clear roadmap that connects research to business advantage.

How Quantinuum Can Help

Achieving quantum maturity is a journey, not a single step, and most organizations don't need to make that journey alone. Quantinuum's consulting services are built to support every stage of it, from advisory and use-case identification, to capability building, technology access, and the co-development of scalable quantum solutions.

Whether your organization is just starting to build awareness or already scaling toward transformation, our team can help you identify exactly where you stand today, and what it takes to move to the next level.

• SoftBank Corp. and Quantinuum articulated a roadmap connecting quantum algorithms, hardware evolution, and commercial applications, helping organizations understand when quantum computing can address real-world challenges.
• The analysis demonstrates that quantum value will emerge in stages, with early opportunities in data analysis and longer-term breakthroughs in scientific computing as fault-tolerant systems mature.
• The work provides a practical planning framework for enterprises preparing to integrate quantum computing alongside AI and classical high-performance computing infrastructure in quantum-AI data centers.

Quantum computing is increasingly moving from exploratory discussion to structured enterprise planning. As organizations begin to assess where and when quantum technologies may deliver real business impact, a new class of work is emerging: integrated roadmaps that connect algorithmic feasibility, hardware development, and commercial opportunity.

A recent white paper from SoftBank Corp. and Quantinuum represents one of the most comprehensive examples of this approach to date. Rather than treating quantum computing as a distant, abstract capability, the study constructs a detailed, quantitative framework for understanding how real-world use cases evolve as hardware matures—and what this means for enterprise strategy.

A Comprehensive Roadmap

The SoftBank–Quantinuum white paper is broad in scope. It attempts to answer a fundamental question:

Which real-world problems can benefit from quantum computation, at what scale, with what accuracy requirements, and under what hardware conditions?

To address this, the study adopts a structured methodology that connects:

• Algorithmic resource estimates (logical qubits, circuit depth, gate counts)
• Explicit error correction assumptions and overhead models
• Hardware roadmaps across multiple generations
• Timelines for when specific classes of problems may become feasible

Two representative domains anchor the analysis:

• ‍Quantum chemistry, particularly excited-state dynamics and photochemical processes‍
• Topological data analysis (TDA), including graph-based structure and anomaly detection

These domains were selected because they combine industrial relevance with computational structures that scale poorly on classical systems but map naturally onto quantum approaches.

Quantum chemistry is closely tied to materials science, energy systems, and the development of sustainable technologies. TDA, by contrast, offers tools for understanding complex data structures in networks, finance, and large-scale systems—where identifying structure and anomalies is increasingly critical.

Together, they illustrate how quantum computing may create value across both deep scientific domains and high-impact data applications.

An important caveat is that the resulting roadmap assumes a widely-studied but inefficient error correcting code. As more error correcting codes come online, the resources required to run algorithms will shrink. That means that the timelines detailed in this work can be thought of as “worst case” scenarios, which adds to the value by setting out a clear limit.

A distinguishing feature of the work is its emphasis on implementation over abstraction. Rather than relying solely on theoretical models, the study explicitly constructs quantum circuits and executes them on Quantinuum’s Helios and H2 system.

Parallel Paths to Value

A notable insight from the study is that quantum value creation will not follow a single linear path.

Instead, two complementary regimes are expected to emerge:

• In quantum chemistry, the focus is on fault-tolerant, logical-qubit-based computation, where error correction is essential for scaling toward meaningful scientific and industrial outcomes.
• In TDA, value can emerge earlier, where partial quantum advantage may already support useful computational tasks without full error correction.

This dual-track structure is important: it shows that quantum computing is not a single “threshold technology,” but a spectrum of capabilities that unlock value at different stages of maturity.

The Vision for Quantum Industrialization

The framework ultimately supports a broader strategic vision: the evolution of Quantum AI Data Centers—hybrid infrastructures where quantum processors operate alongside AI and classical HPC systems.

For enterprises, the implication is clear: quantum computing readiness is no longer about speculation. It is about structured preparation, disciplined modeling, and early engagement with the full stack of capabilities that will define the next generation of computational infrastructure.

Every year, The IEEE International Conference on Quantum Computing and Engineering – or IEEE Quantum Week – brings together engineers, scientists, researchers, students, and others to learn about advancements in quantum computing. This year’s conference, from September 13th - 18th in Toronto, Canada, will focus on translating research into real-world impact through the convergence of generative AI, distributed quantum systems, and quantum software engineering.

Throughout IEEE Quantum Week, our quantum experts will be on-site to share insights on upgrades to our hardware, enhancements to our software stack, our path to error correction, and more.

Meet our team at Booth #501 and join the below sessions to discover how Quantinuum is forging the path to fault-tolerant quantum computing with our integrated full-stack.

Keynote with Quantinuum's CEO, Dr. Rajeeb Hazra

September 14^th

5:00 – 6:30pm | 800 Hall G
Quantum computing has passed the point where error correction is theoretical. What comes next depends on systems that hold logical performance steady and do real work at scale. On September 14^th, join Quantinuum’s CEO Dr. Rajeeb Hazra for his keynote session on “Logically Speaking: The Next Era of Error Correction” where he will explore what the next era of quantum computing requires: shared definitions of logical performance, and benchmarks built on real workloads.

Speaking Sessions

September 13^th

11:00 – 11:12am | Location: 601A/B
Workshop: QGenAI: Synergies between Quantum Computing and Generative Artificial Intelligence
Finding Compatible Datasets for Quantum Generative Modeling
Presenting Author: Chen-Yu Liu

September 14^th

1:00 – 1:20pm | Location: 701B
Workshop: Q3-Control: Integrated Systems for Quantum Computing, Sensing, and Networking
Cryo-ASICs for Scalable Control
Speaker: Dr. Patty Lee

September 15^th

2:30 – 3:30pm | Exhibit Hall (informal event)
Workshop: Q3-Control: Integrated Systems for Quantum Computing, Sensing, and Networking
The Quantum Spectrum
Speaker: Dr. Patty Lee

3:00 – 4:30pm | Location: 801A
Where Quantum-HPC Integration Actually Stands in 2026
Panelist: Neal Erickson

3:00 – 4:30pm | Location: 718A
From Research to Commercialization: Defining the Quantum Workforce for the Next Five Years
Panelist: Kortny Rolston-Duce

September 16^th

10:00 – 11:30am | Location: 801B
Who Will Turn Quantum Computing into Value? Specialists, Domain Experts, and the Workforce Gap
Panelist: Enrico Rinaldi

September 17^th

10:00 – 11:30am | Location: 701B
International Workshop on Quantum Computing for Power Systems: From Optimization Algorithms to Grid-Scale Applications | Session 1
Organizer: Kortny Rolston-Duce

10:00 – 11:30am | Location: 714A
openQSE: Co-Designing the Quantum-HPC Software Stack from Applications to Control Systems | Session 1
Speaker: Neal Erickson

10:00 – 11:30am
The Impact of Qubit Connectivity on Quantum Advantage in Noisy IQP Circuits
Presenting Author: Leonardo Placidi

10:15 – 11:00 am | Location: 601A/B
Workshop: AI for Circuit Synthesis, Optimization, and Discovery
Automated near-term quantum algorithm discovery
Speaker: Konstantinos Meichanetzidis

11:00 – 11:15am | Location: 601A/B
Workshop: AI for Circuit Synthesis, Optimization, and Discovery
Fast Stabilizer State Preparation via AI-Optimized Graph Decimation
Presenting Author: Jasmine Brewer

1:00 – 1:15pm | Location: 601A/B
Workshop: AI for Circuit Synthesis, Optimization, and Discovery
Reinforcement Learning for Adaptive Composition of Quantum Circuit Optimisation Passes
Speaker: Gabriel Matos

1:00 – 1:20am | 718B
Quantum Software 2.6: current challenges and headways in quantum software | Session 1
Quantum compilation and hybrid compilation
Speaker: Ross Duncan

1:00 – 2:30pm | Location: 714A
openQSE: Co-Designing the Quantum-HPC Software Stack from Applications to Control Systems | Session 2
Speaker: Phillipp Seitz

1:45 – 2:00pm | Location: 601A/B
Workshop: AI for Circuit Synthesis, Optimization, and Discovery
Graph-Theoretic Quantum Circuit Optimization with the ZX-Calculus and Gumbel AlphaZero
Speaker: Alexander Koziell-Pipe

2:00 – 2:15pm | Location: 601A/B
Workshop: AI for Circuit Synthesis, Optimization, and Discovery
Reusable Equivariant Neural Compilers for Matrix-Group Quantum Circuit Synthesis
Speaker: Richie Yeung

3:30 - 4:00pm | Location: 715B
Workshop: 4th Workshop on Quantum Computing for Natural Sciences: Technology and Applications
Quantum Algorithms
Speaker: Setso Metodi

September 18^th

10:00 – 11:30am | Location: 801B
Scalable QPU architectures
Panelist: Setso Metodi

AI + quantum computing: Quantinuum, NVIDIA, and Pfizer have combined transformer-based generative AI with quantum computing to automatically generate high-quality quantum chemistry circuits more efficiently than traditional optimization methods.

Practical pharma impact: The approach was used to prepare molecular ground states and validated on Quantinuum’s Helios hardware, demonstrating a path toward larger-scale computational chemistry and drug discovery.

Long-term vision: The team aims to build quantum foundation models that learn from increasingly complex quantum data, eventually enabling AI to design circuits for molecules too large for classical simulation.

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Quantum computing has long promised a future that expands what we can do with compute — for example, in molecular simulation, materials discovery, or pharmaceuticals development. But between that promise and practical utility sits a stubborn bottleneck: quantum state preparation.

To run any algorithm on a quantum computer, you must first put the qubits in the right starting state. Think of it like setting up a Rube Goldberg machine- except in this case, you’re not sure exactly which initial setup will give you the results you want. This is what makes quantum state preparation so important: your choice of initial state dictates the accuracy and cost of the rest of the calculation.

We teamed up with NVIDIA and Pfizer to tackle this problem, with an eye towards developing meaningful industrial workflows. The result is a new generative quantum AI framework, called ADAPT-GQE, which we consider to be a canonical instance of GenQAI. ADAPT-GQE uses quantum data to train transformer models that ultimately synthesize quantum chemistry circuits faster, with better outcomes, in a sort of ‘virtuous cycle’.

Ultimately, this means we have developed a new interface between quantum computing and AI. By treating quantum circuit generation as a language modelling problem, we now have a system that can generate high-quality ground-state preparation circuits - with comparable or improved state preparation accuracy.

The Magic – and Difficulty – of Computational Chemistry

The goal of computational chemistry is to learn about chemical properties without performing expensive, time-consuming, and sometimes dangerous “wet-lab” experiments.

In principle, you can replace the majority of your physical experiments with computer simulations, saving billions of dollars and years of time.

In reality, computational chemistry is very tricky. To accurately simulate a chemical inside of a computer, you have to build it from the ground up. You start with a collection of atoms (in the case of imipramine, you have 19 Carbon atoms, 24 Hydrogen atoms, and 2 Nitrogen atoms). Then, like Nature’s ‘lego’, you assemble those atoms into a molecule: you set bond lengths, strengths, angles, interactions, and so on.

This is not straightforward: a single molecule can exist in many forms; with different angles, rotations, etc. We will call these different forms ‘conformations’.

Then, to actually estimate chemical properties, or to explore chemical reaction pathways, you have to reproduce the detailed physics that goes on at the atomic level: take your chosen conformation then figure out how each orbital is occupied, how the electrons are interacting with each other or the atomic nuclei, how is the addition of heat or a catalyst going to affect things.... it gets complicated, quickly.

Despite all this, computational chemistry is a powerhouse in pharmaceutical development. Right now, pharmaceutical companies save money and time by simulating as much as they can on computers, avoiding time consuming and expensive laboratory experiments. However, even with ~50 years of development, the existing classical methods have very real limitations.

This is where quantum computing comes in: this new computational paradigm can elide those limitations because it has many of the “hard parts” (like superposition or entanglement) natively encoded. Used correctly, quantum computing promises to break old barriers, further improving margins for pharma companies across the globe while contributing to meaningful, impactful, discoveries.

A Virtuous Cycle: Using Quantum Data to Train AI, Which Then Designs Better Quantum Circuits

While quantum computational chemistry is one of the strongest candidates for near-term quantum advantage, current hardware is still in the earlier stages of development. With limited qubits and error rates, algorithm designers need to make every gate count, keep circuits shallow, and be able to tolerate some level of noise.

This is where generative AI enters the picture.

Instead of hand-designing chemistry circuits and laboriously experimenting to see how well they run, there is another idea: what if we trained an AI to solve the problems that quantum computational chemistry faces?

Using this approach, not only can we save time and resources; but we can shorten the timeline to realize practical results. With better state prep and other circuits, applications that were once considered far in the future come into view.

Our first attempt at this is called ADAPT-GQE. The central idea behind ADAPT-GQE is deceptively simple: instead of laboriously searching for good quantum circuits from scratch, train a transformer model to generate them directly.

Importantly, the framework is model-agnostic, which we showed by deploying it on complementary transformer architectures - Nemotron (a pretrained LLM) and Gemma (trained from scratch).

From Iterative Optimization to Generative Models

The initial goal here is to find the ‘ground state’ of the molecule imipramine (this is the electronic state with the smallest amount of energy stored inside it). To do this, you have to find the right ‘state preparation circuit’, as described above.

Until now, a leading method for finding the ground state with quantum computers was the ‘Variational Quantum Eigensolver (VQE)’, a hybrid quantum-classical approach. The VQE process starts with a ‘guess’ circuit for a particular conformation of the molecule. The quantum computer runs the circuit to measure the associated energy of the molecule. This result is fed back into a classical optimizer that then tweaks the circuit parameters, hopefully resulting in one with a lower molecular energy. This loop repeats until a minimum energy is found.

Unfortunately, VQE has a few severe limitations that make it infeasible for widespread use. The recently proposed ADAPT-VQE was a crucial step forward meant to address some of the issues with “plain” VQE. In ADAPT-VQE, instead of starting with a guess for the initial circuit, the process builds a circuit in steps by selecting operators from a pool(typically using gradient information) and optimizing. This approach can be more effective, but unfortunately still grows too large too quickly.

This is where the joint team jumped in.

Combining the best of all worlds, the team’s new framework, ADAPT-GQE, combines AI with the ADAPT-VQE to create something entirely new – and something that, so far, is a scalable, hardware-validated pathway toward automated quantum circuit synthesis.

First, transformers (in this case, Nemotron and Gemma) are trained via supervised fine-tuning on ADAPT-VQE data. In this way, the old method isn’t thrown away but is instead treated as a high-quality data-producing “oracle”.

Then, once the transformer has been initially trained, it defines a distribution over circuits, each one with some probability of corresponding to the ground state. This distribution can be used in a fine-tuning loop, for example, reinforcement learning. In reinforcement learning, the framework takes a circuit from that distribution, runs it, and measures the energy. It feeds the results back into the transformer, which adjusts its distribution. Over time, the model learns to prioritize circuits that prepare increasingly accurate ground states.

Crucially, reinforcement learning allows the system to surpass its original training data instead of merely imitating it. The model is no longer acting as a compressed lookup table for ADAPT-VQE. It begins exploring novel circuit configurations that may outperform the teacher algorithm itself. This is one of the most important conceptual shifts in the project.

In this case, instead of running all the initial circuits on Quantinuum’s Helios, the reinforcement learning circuits were run using NVIDIA accelerated computing and the CUDA-Q platform, simulating a quantum processor.

Finally, once the transformers are optimized via reinforcement learning, the best resulting circuits are validated for accuracy and feasibility, by running them using InQuanto and Nexus on Quantinuum’s newest hardware, Helios. With InQuanto v5.2, users can now interface directly with both the Helios quantum computer and the Selene quantum emulator through Nexus.

This powerful combination of InQuanto and Nexus enabled the execution one of the largest AI-generated quantum chemistry circuits to date on a quantum computer; helping to turn the promise of quantum computing into a practical tool for pharmaceutical development.

Teaching a Transformer

Looking farther in the future, the researchers envision something much larger than a single molecular benchmark.

For bigger and more complex molecules, ADAPT-VQE won’t work in the first place as the initial training “oracle”. In addition, the molecular energy calculations used in the reinforcement learning grow too large for classical systems simulating quantum computers, so the quantum processor becomes essential.

Luckily, this is not a problem. The ultimate goal of the ADAPT-GQE framework is to develop a “curriculum” for the transformers. This means instead of re-training them for every new molecule, you instead keep what you already learned, and expand your knowledge from there.

By initially teaching it on molecules that are smaller, and that can be fully simulated, you ensure it learns on good data that can be double checked using known methods. From there, you can carefully build up the complexity to see how the transformer learns. Eventually, you hope to train it on molecules that can’t be simulated classically, using purely quantum data, all the while getting closer to the complexity levels you’re chasing.

This penultimate result is called a ‘foundation model’, which is a massive AI neural network trained on vast, broad datasets that can be adapted to a wide variety of downstream tasks. In this case, the team is building the very ‘foundations’ of a model that can solve the ‘electronic structure problem’, which is the core computational challenge lying at the heart of quantum (and classical) computational chemistry.

A New Interface Between AI and Quantum Computing

What makes this work particularly interesting is that it treats quantum circuit generation as a language modeling problem: circuits become sequences, transformers learn distributions over those sequences, and reinforcement learning optimizes them against physical reward functions.

The result is an AI system capable of proposing quantum circuits that were never explicitly programmed by humans.

That does not mean generative AI is replacing physics or chemistry. Instead, it is becoming a new interface layer for navigating unimaginably large search spaces that traditional optimization methods struggle to explore efficiently.

For quantum chemistry, that could become transformative.

If successful, frameworks like ADAPT-GQE may eventually allow researchers to synthesize useful quantum circuits for molecular systems too large for classical computation, accelerating everything from materials discovery to pharmaceutical design.

The broader implication is difficult to ignore: foundation models may eventually extend beyond language, images, and code — and into the fabric of physical reality itself.

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.

• 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.