• 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

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

1:30 - 2:00pm | Location: 701B
Fault-Tolerant Compilation with Guppy FT, and Real-Time NVIDIA Decoding
Workshop on Control System for Quantum Computing
Presenter: Thomas Skripka
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2:30 – 3:00pm | Exhibit Hall
The Quantum Spectrum: Where Do You Stand?
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.

‍

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.