Teaching AI with Quantum Data
We’ve teamed up with NVIDIA and Pfizer to expand the frontier of computational chemistry, combining AI and quantum computing in a canonical instance of GenQAI
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.
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.
Visit Quantinuum at SC26 – Booth 4838
Quantinuum is heading to SC26 in Chicago, and we'd love to see you at Booth 4838 in the Quantum Junction. Stop by during exhibit hours to catch partner theater sessions throughout the day, see what's new with Helios and Nexus, and talk with our team about how quantum computing is expanding its footprint in the HPC datacenter, including hybrid quantum-classical applications.
In our booth:
Join our experts and partners at our kiosks and for short sessions in our theater running throughout the day. Topics include:
- Next Gen Enterprise Computing Partners – how leading enterprises are bringing quantum into their computing strategy
- Error Correction: The Path to Fault Tolerance – where quantum error correction stands today and what comes next
- Develop for the Fault Tolerant Era with Nexus – building, running, and managing quantum workflows on the Nexus platfor
- Helios: The Most Accurate Quantum Computer – a closer look at our latest system and what its performance makes possible
- Startup Partners: Accelerating Quantum Ecosystem Innovation – how startups are building on Quantinuum technology
- Real Commercial Impact – quantum applications in pharma, chemicals, materials, and finance
Coffee and cookies on us
Need a break from the show floor? Our barista will be serving up coffee, and we'll have fresh cookies every afternoon. Grab a cup, pull up a seat for a theater session, and chat with the team.
Catch Quantinuum in the SC26 program
Our experts are also speaking in the SC26 technical program. Add these sessions to your schedule (all times CST):
Speaking Sessions
November 15th
9:45 – 10:00am | Room: W178a
Quantum-HPC Workflows Across Multiple Quantum Computing Platforms: Two Case Studies – part of the 2nd International Workshop for Software Frameworks and Workload Management on Quantum-HPC Ecosystems
Presenting: Philipp Seitz
November 16th
3:30 – 4:00pm | Room: W179a
Software for Large Scale Application – part of the 2nd Annual Workshop on Large-Scale Quantum-Classical Computing (LSQCC)
Presenting: Juan Pederson
November 19th
3:30 – 4:00pm | Room: W181abc
Bringing Quantum Computing to the HPC Datacenter
Presenting: Kentaro Yamamoto
See you in Chicago
Whether you're exploring quantum for the first time or planning how to integrate it with your HPC environment, our team is ready to talk. Find us at Booth 4838 in the Quantum Junction, November 16–19.
- RIKEN and Quantinuum have upgraded Reimei to System Model H2, bringing quantum advantage into sight with their hybrid supercomputing platform.
- Using the new quantum processor, a Quantinuum-led frontier project team is combining quantum and classical data to map complex material behavior, using Reimei for classically challenging regimes, the JHPC-quantum GPU supercomputer ROQUO and the supercomputer Fugaku for large-scale simulation and AI, and generative models to predict unexplored conditions.
- A second frontier project is focused on creating a scalable hybrid workflow for biomolecular chemistry, combining quantum computing, supercomputing, and AI to simulate the excited-state properties of complex molecules—with potential applications in drug discovery, advanced materials, and solar energy. Once these projects conclude, Reimei will start regular operations under the Test User Program.
The future of supercomputing just got an upgrade in Japan.
RIKEN and Quantinuum have upgraded the Reimei quantum computer to Quantinuum’s System Model H2, bringing quantum advantage into sight with their hybrid supercomputing platform. Their combination of Reimei, the supercomputer Fugaku, and the ROQUO GPU supercomputer is a world-leading platform that reaches far beyond what any modality can currently offer on its own.
With 56 fully-connected high-fidelity qubits, Reimei is already supporting two frontier projects in quantum materials and biomolecular chemistry. Both are designed to move hybrid computing beyond infrastructure demonstrations and toward high-value scientific applications.
Expanding the landscape of supercomputing
Many scientific problems contain both classically ‘accessible’ and classically ‘inaccessible’ regions.
For example, a classical supercomputer may efficiently explore broad areas of a model’s parameter space but struggle near a phase transition, where correlations grow and the required computational resources increase sharply. In chemistry, classical methods can model much of a molecule accurately but encounter difficulties in small, strongly correlated regions that determine its most important properties. In contrast, quantum computers can often handle these problems deftly, thanks to their native “quantumness”.
We have been working to bring quantum computers together with classical computers, using them as specialized accelerators inside broader heterogeneous computing environments.
RIKEN’s platform is our first instantiation of that vision, bringing together three complementary resources:
- Fugaku, RIKEN’s classical supercomputer, provides large-scale classical modeling, data processing, and established scientific computing methods.
- ROQUO, RIKEN’s GPU supercomputer, supports tensor-network simulation, AI training, and massively parallel computation.
- Reimei, RIKEN’s quantum computer, performs selected calculations involving quantum states and correlations that become prohibitively expensive to represent classically.
Quantinuum Nexus’s hybrid workflow management system coordinates these resources, allowing jobs running on classical infrastructure to submit quantum tasks to Reimei and incorporate the results into a larger calculation.
This foundation has been operational for some time now. A major result from earlier this year used Reimei and Fugaku together to investigate chemical reactions inside biomolecules. Recently, we expanded our work using Fugaku and QPUs: coordinating Reimei and ibm_kobe in a combined VQE–QPE workflow to calculate a simple system’s ground-state energy. In all these cases, quantum hardware augmented the classical supercomputer, with each processor assigned the part of the calculation best suited to its strengths.
The addition of ROQUO now extends this architecture with GPU-accelerated simulation, optimization, and AI. Combined with the upgrade of Reimei to System Model H2, we have launched two frontier projects to push the forefront of scientific computing and prove out Reimei’s capability.
Project 1: GenQAI for materials discovery
The first frontier project brings together quantum simulation, classical tensor networks, and generative AI to investigate many-body physics.
The goal is to discover a sort of ‘treasure map’ for a material: the phase diagram. A phase diagram is a multidimensional map that shows how a material’s properties change when variables like temperature, pressure, or magnetic field are changed. Phase diagrams can hold the secret to exotic properties like superconductivity; can point the way to novel materials for memory devices and energy transfer, or can help to elucidate new medical imaging techniques.
Phase diagrams are extremely valuable, but they can be difficult to calculate or measure. To measure one, you would need a laboratory that could detect material properties at hundreds of points while also generating repeatable parameter changes. Computationally, the problem is similar, but (as mentioned above) one often encounters regions where correlations extend across larger portions of the system, making it incredibly difficult to compute. On top of this, during pre-thermal dynamics or phase transitions, entanglement can grow rapidly, further stretching classical computing capacity.
Reimei is well suited to probing these difficult regimes. We have already used System Model H2 to explore quantum magnetism at scales that frustrate classical approaches. Its high-fidelity operations and flexible qubit connectivity enable the execution of demanding quantum many-body circuits at a scale where matched-accuracy classical simulation can become impractical. But this quantum data is also expensive to generate. The goal is therefore to use it selectively.
ROQUO can perform dense tensor-network simulations across classically tractable regions, parallelizing calculations over model parameters and bond dimensions. This allows Reimei to focus on regimes requiring deep time evolution circuits, where long-range correlations and growing entanglement can sharply increase the cost of classical simulation. Classical data alone is not enough to train a robust generative model across the phase diagram, and that is why quantum data becomes essential.
The team has already collected hardware data for a 6 × 6 two-dimensional spin model, trained a Transformer model for zero-noise extrapolation across the parameter space, and applied active learning to prioritize additional quantum data collection.
Using these results, the team trained a generative AI model capable of learning a parametric representation of the fluctuations and many-body correlations across the combined dataset: the classically reachable and the classically hard regions are both well described by our trained generative model.
Once trained, the generative model is used at inference time on ROQUO to sample the phase diagram at conditions that have not yet been simulated and obtain results for material properties with good accuracy. Step by step, the phase diagram for a material is filled out, generating a ‘treasure map’ of its key behaviors.
For materials researchers, this ‘map’ can add up to a lot. Quantum computers, such as Reimei, could contribute to more accurate predictions of phase diagrams and response functions, helping researchers understand how materials respond to external conditions, a key determinant in electronics, sensing, medical imaging, energy infrastructure, and more.
Project 2: A hybrid blueprint for biomolecular chemistry
The second frontier project applies the same hybrid philosophy to one of quantum computing’s most anticipated applications: the accurate simulation of complex molecules.
Researchers from RIKEN, NVIDIA, and Quantinuum are working together to develop a scalable QPU–GPU–HPC workflow for predicting realistic chemical properties of biomolecular systems. Our initial target is zinc porphyrin, a light-absorbing molecule whose response to light is influenced by its surrounding environment.
While many current projects in quantum computational chemistry are focused on ground state searches, this project focuses on excited states—the electronically energized states involved in processes such as light absorption. These states are typically more complicated to calculate than a molecule’s ground state, but they are essential to understanding how photoactive molecules function in the real world.
As before, the hybrid workflow assigns each part of the calculation to the most appropriate processor:
- Fugaku and conventional HPC methods represent the chromophore’s larger molecular environment and perform the broader chemistry workflow.
- ROQUO’s NVIDIA GPUs support optimization, configuration-interaction calculations, AI-assisted circuit design, and spectral reconstruction.
- Reimei concentrates on strongly correlated electronic regions and searches for important electron configurations that classical methods may miss.
This gives the QPU a focused role: discovering important determinants (candidate electronic configurations) that can enrich the classical calculation. The HPC systems then use those configurations to calculate energies and reconstruct experimentally meaningful properties, including the molecule’s absorption spectrum.
This separation of responsibilities makes the workflow scalable. The QPU is not asked to represent the entire biomolecular environment. Instead, it targets the part of the electronic structure problem where quantum sampling could add information that is difficult to obtain through classical methods alone. To help tackle this, we developed a new hybrid method to solve complicated electronic structure problems.
This approach could provide a blueprint for studying functional biomolecules at the accuracy needed for industrial research. Applications could eventually span drug discovery, biomolecular engineering, advanced materials, and the design of more efficient light-harvesting systems such as solar cells.
A platform for the next era of discovery
Hybrid computing is sometimes described as a future architecture. At RIKEN, these foundations have been operational since 2025 – and they just got a massive upgrade.
Reimei supplies high-fidelity quantum computation. Fugaku provides the scale of a world-leading supercomputer. ROQUO adds accelerated computing and AI. The Nexus workflow management system brings the systems together into coordinated scientific workflows.
Together, they demonstrate a broader principle: the value of quantum computing will not be measured by what a QPU can do in isolation, but by what becomes possible when quantum computers are integrated into the world’s most advanced computing environments.
With Reimei, RIKEN and Quantinuum are building that future now—and building it with Japan.
*Reimei deployment is supported by the New Energy and Industrial Technology Development Organization (NEDO), an organization under the jurisdiction of Japan's Ministry of Economy, Trade and Industry (METI)'s "Development of Integrated Utilization Technology for Quantum and Supercomputers" as part of the "Project for Research and Development of Enhanced Infrastructures for Post 5G Information and Communications Systems."
Fault-tolerant quantum computing is entering a new phase. At Quantinuum, we have demonstrated almost every piece needed for large-scale fault tolerant hardware, from a universal fault tolerant gate set, to a proven fault tolerant architecture, to large-scale materials simulations with encoded qubits, at world-leading fidelity.
However, that’s only half the battle. Developers need practical tools that make this technology accessible without needing to become experts in quantum error correction.
A good quantum computing software stack should capture and reuse QEC1 expertise, turning the complexity of fault tolerance into capabilities that developers can build with.
"Our ambition is for Nexus to be the platform developers turn to for the fault-tolerant era." - Dr. Rajeeb Hazra, President & CEO, Quantinuum
Here at Quantinuum, we are working hard to make that a reality. Since its launch in 2024, Nexus has grown from managing cloud-based quantum workflows into an environment for end-to-end application development and deployment. Our new multi-year roadmap builds on that foundation, connecting software capabilities with each generation of Quantinuum hardware through Apollo, our universal, fault-tolerant quantum computer planned for 2029. Nexus will progressively handle more of the complexity of fault-tolerant development, helping developers build applications that grow in capability as the hardware advances.
With Nexus, you can explore quantum chemistry and materials using InQuanto, and you can make it fault tolerant using our pre-built fault tolerant primitives. You can use GPUs to decode errors in real time, while benefitting from hardware enabled error suppression like Leakage Repump or Dynamical Decoupling. You can also connect your HPC to our QPUs and use Nexus’ Hybrid workflow management system to enjoy the best of both worlds seamlessly.
Of course, all of the above needs a language framework that is fault tolerant in the first place. That’s why we are thrilled to introduce Guppy FT – Nexus’ fault tolerant quantum programming framework, and Guppy Algorithms, Nexus’ library of reusable algorithmic primitives.
Developing for the fault tolerant era: Guppy FT
“The field of QEC is quite abstract, and it is hard for non-experts to gauge what is feasible on current devices. Guppy FT promotes a game-changing paradigm: QEC is no longer just theory, it is also engineering, it is something you can use.” - the Guppy FT team
As we have been saying, the NISQ2 era is coming to an end. The next challenge is to turn fault tolerance from a hardware milestone into a capability developers can use.
We have built the physical QEC architecture: high-fidelity physical qubits, distance scaling and low logical error rates, all-to-all connectivity and reconfigurable qubits, all the primitives working harmoniously, real-time decoding, circuit optimization, and so on. The next step is ensuring our users can easily enjoy the above: with a fault tolerant programming language framework (Guppy FT) inside a fault tolerant user platform (Nexus).
Guppy FT brings three complementary capabilities together:
- A framework for QEC experts to define fault-tolerant architectures.
- Automatic compilation that takes programs written in standard Guppy and turns them into programs running on fault-tolerant architectures.
- Proprietary codes from our own QEC experts that get you the best performance out of our world-leading hardware.
The result is a programming experience in which developers can write quantum algorithms without having to understand the details of the underlying error-correction architecture, while QEC experts retain the ability to define and optimize the architectures that power those programs.
This separation between what the program does and how it is made fault tolerant is central to the approach.
With Guppy FT, QEC experts define the fault-tolerant architecture once, and application developers can program on it without needing to become QEC experts themselves. Developers can continue to write programs in standard, QEC-agnostic Guppy. The Guppy FT framework then automatically lowers those programs into an encoded, fault-tolerant form suitable for execution.
As fault-tolerant techniques improve, the same high-level programming model can therefore serve as a stable interface between applications and increasingly sophisticated QEC implementations. This approach also makes it possible to evolve the underlying architecture without requiring every application developer to rewrite their programs around its implementation details – a crucial benefit as we stay at the forefront of a quickly evolving field.
Available to users today, Guppy FT is an important step toward software designed for the fault-tolerant era. You can use already-perfected codes, like our high-rate quantum error detection code, through Nexus, with more codes coming online as they are developed.
Optimize your work with Guppy Algorithms & Realtime Analytics
We want you doing more than just writing fault tolerant programs – we want to help you write quickly and optimally.
With Guppy Algorithms, you can save needless repetition of work by constructing complex application-level software workflows in an application-agnostic way. Built on Guppy, Guppy Algorithms combines type-safe Guppy primitives with Python builders to help developers construct quantum algorithms out of already optimized building blocks. Guppy Algorithms is also QEC agnostic, enabling users to evolve their fault tolerance approach as the platform grows, without needing to re-write finished programs.
Current highlights include primitives that help you with:
- Trotterisation for Hamiltonian Simulation
- Quantum linear algebra
- Quantum arithmetic
Using Guppy Algorithms is simple. First, you build your abstract (application-specific) algorithm. Then, you simply compose it with Guppy Algorithms: import your desired implementations of circuit sub-components and bring your workflow to life.
"Guppy FT and Guppy Algorithms add a necessary abstraction on top of Guppy’s flexible programming model, so that application development and QEC research can advance in lockstep without either having to bake in assumptions about the other.” - Seyon Sivarajah, Chief Architect of Developer Tools
Soon, we plan to expand Guppy Algorithms to include end-to-end workflows of reusable components.
Of course, a set of optimized building blocks isn’t enough to ensure every program you write is optimal for execution. To that end, Nexus is introducing two tools to help users understand and optimize hardware execution:
- Real-time metrics that provide execution statistics, including gate counts and shot duration.
- ShotPlot, our tool for visualizing gate scheduling, helping users identify idle periods and improve circuit layout.
With these functionalities, you can try new approaches to minimizing gate counts or runtime with real-time feedback that helps you further optimize your result. Taken together, these new tools ensure that Nexus provides an optimal programming experience, from the circuit level to the user experience.
Using Guppy FT to reduce errors on quantum hardware: an end-to-end demonstration on Helios
To demonstrate Guppy FT, the team ran a Steane-encoded quantum algorithm, HHL, on Quantinuum’s Helios quantum computer.
The team compared three versions of the HHL program, with different settings for Guppy FT’s Steane code ([[7, 1, 3]]) encoding, each with 500 shots. The first used no explicit QEC cycles and performed on-demand, sequential state preparation. The second used Guppy FT’s state factories to parallelize state preparation, and the third also applied three QEC cycles to each of the five qubits.
Each shot is comprised of three stages: the HHL program, mid-circuit error detection, and the inverse of the HHL program. Mid-circuit error detection measures the ancilla qubits used by the HHL program, which it ought to disentangle from the data qubits. If an error is detected, the shot is discarded; otherwise, it moves on to the final stage and is marked as accepted. Fidelity is computed as the proportion of accepted shots that the inverse HHL program successfully returns to the initial state.
The results show that both QEC cycles and parallelized state preparation improve performance. QEC cycles actively correct errors while parallelization shortens execution time, reducing the chance of errors. Together, these results demonstrate how Guppy FT can implement fault-tolerant architectures for practical, end-to-end programs running on real quantum hardware.
Deploy Your Hybrid Quantum-Classical Application
The future of quantum computing is more than fault tolerant: it’s hybrid. Close co-ordination between classical and quantum computing resources will be essential for most killer use-cases, with a combination of AI, HPC, and QPU each working on the sub-components they are best suited for.
We’ve been perfecting hybridization for years now, including hybridizing our QPUs with the Fugaku supercomputer at RIKEN. Along the way, we have developed a robust event-based hybrid compute runtime, now available through Nexus. Our updated hybrid compute framework features:
- Multiple storage implementations
- Workflow attempts and restart improvements
- Runtime configuration files
- OTEL tracing and observability
- Workflow cancellation
- SQLite based state management
Quantinuum Nexus: A foundation for what comes next
Nexus brings together the capabilities and infrastructure needed to develop, analyze, run, and manage hybrid quantum-classical programs across the stack. By unifying quantum programming, validation, fault tolerance, compilation, and execution in one place, with interoperability across third-party environments, Nexus is making fault tolerance a practical reality.
Nexus users are already using our quantum processors to explore applications in finance, nuclear physics, particle physics, benchmarking, computational chemistry, and more. As our Nexus roadmap evolves, we plan on deploying ecosystem tools and libraries, as well as an applications marketplace where developers can monetize their work.
“Quantum computers are on the brink of achieving fault tolerance at scale. It's exciting: we are entering terra incognita for what we can compute. The new challenge is putting our high-level quantum algorithms into the right form to successfully run on error-corrected hardware. Developer platforms like Nexus help us bridge that gap between quantum algorithm design and deployment
-José Ignacio Latorre, Director, Centre for Quantum Technologies at the National University of Singapore at the National University of Singapore
By bringing everything together, Nexus is becoming a robust platform in which fault tolerance is a feature, rather than a barrier to entry.
Get ready for the fault tolerant future.
1 Quantum Error Correction
2 Noisy Intermediate Scale Quantum