Quantinuum researchers tackle AI’s ‘interpretability problem’, helping us build safer systems
The Artificial Intelligence (AI) systems that have recently permeated our lives have a serious problem: they are built in a way that makes them very hard - and sometimes impossible - to understand or interpret. Luckily, our team is tackling this problem, and we’ve just published a new paper that covers the issue in detail.
It turns out that the lack of explainability in machine learning (ML) models, such as ChatGPT or Claude, comes from the way that the systems are built. Their underlying architecture (a neural network) lacks coherent structure. While neural networks can be trained to effectively solve certain tasks, the way they do it is largely (or, from a practical standpoint, almost wholly) inaccessible. This absence of interpretability in modern ML is increasingly a major concern in sensitive areas where accountability is required, such as in finance and the healthcare and pharmaceutical sectors. The “interpretability problem in AI” is therefore a topic of grave worry for large swathes of the corporate and enterprise sector, regulators, lawmakers, and the general public.
These concerns have given birth to the field of eXplainable AI, or XAI, which attempts to solve the interpretability problem through so-called ‘post-hoc’ techniques (where one takes a trained AI model and aims to give explanations for either its overall behavior or individual outputs). This approach, while still evolving, has its own issues due to the approximate nature and fundamental limitations of post-hoc techniques.
The second approach to the interpretability problem is to employ new ML models that are, by design, inherently interpretable from the start. Such an interpretable AI model comes with explicit structure which is meaningful to us “from the outside”. Realizing this in the tech we use every day means completely redesigning how machines learn - creating a new paradigm in AI. As Sean Tull, one of the authors of the paper, stated: “In the best case, such intrinsically interpretable models would no longer even require XAI methods, serving instead as their own explanation, and one of a deeper kind.”
At Quantinuum, we’re continuing work to develop new paradigms in AI while also working to sharpen theoretical and foundational tools that allow us all to assess the interpretability of a given model. In our recent paper, we present a new theoretical framework for both defining AI models and analyzing their interpretability. With this framework, we show how advantageous it is for an AI model to have explicit and meaningful compositional structure.
The idea of composition is explored in a rigorous way using a mathematical approach called “category theory”, which is a language that describes processes and their composition. The category theory approach to interpretability can be accomplished via a graphical calculus which was also developed in part by Quantinuum scientists, and which is finding use cases in everything from gravity to quantum computing.
A fundamental problem in the field of XAI has been that many terms have not been rigorously defined, making it difficult to study - let alone discuss - interpretability in AI. Our paper presents the first known theoretical framework for assessing the compositional interpretability of AI models. With our team’s work, we now have a precise and mathematically defined definition of interpretability that allows us to have these critical conversations.
After developing the framework, our team used it to analyze the full spectrum of ML approaches. We started with Transformers (the “T” in ChatGPT), which are not interpretable – pointing to a serious issue in some of the world’s most widely used ML tools. This is in contrast with (sparse) linear models and decision trees, which we found are indeed inherently interpretable, as they are usually described.
Our team was also able to make precise how other ML models were what they call 'compositionally interpretable'. These include models already studied by our own scientists including DisCo NLP models, causal models, and conceptual space models.
Many of the models discussed in this paper are classical, but more broadly the use of category theory and string diagrams makes these tools very well suited to analyzing quantum models for machine learning. In addition to helping the broader field accurately assess the interpretability of various ML models, the seminal work in this paper will help us to develop systems that are interpretable by design.
This work is part of our broader AI strategy, which includes using AI to improve quantum computing, using quantum computers to improve AI, and – in this case - using the tools of category theory and compositionality to help us better understand AI.
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.
Quantinuum is focusing on redefining what’s possible in hybrid quantum–classical computing by integrating Quantinuum’s best-in-class systems with high-performance NVIDIA accelerated computing to create powerful new architectures that can solve the world’s most pressing challenges.
The launch of Helios, Powered by Honeywell, the world’s most accurate quantum computer, marks a major milestone in quantum computing. Helios is now available to all customers through the cloud or on-premise deployment, launched with a go-to-market offering that seamlessly pairs Helios with the NVIDIA Grace Blackwell platform, targeting specific end markets such as drug discovery, finance, materials science, and advanced AI research.
We are also working with NVIDIA to adopt NVIDIA NVQLink, an open system architecture, as a standard for advancing hybrid quantum-classical supercomputing. Using this technology with Quantinuum Guppy and the NVIDIA CUDA-Q platform, Quantinuum has implemented NVIDIA accelerated computing across Helios and future systems to perform real-time decoding for quantum error correction.
In an industry-first demonstration, an NVIDIA GPU-based decoder integrated in the Helios control engine improved the logical fidelity of quantum operations by more than 3% — a notable gain given Helios’ already exceptionally low error rate. These results demonstrate how integration with NVIDIA accelerated computing through NVQLink can directly enhance the accuracy and scalability of quantum computation.
This unique collaboration spans the full Quantinuum technology stack. Quantinuum’s next-generation software development environment allows users to interleave quantum and GPU-accelerated classical computations in a single workflow. Developers can build hybrid applications using tools such as NVIDIA CUDA-Q, NVIDIA CUDA-QX, and Quantinuum’s Guppy, to make advanced quantum programming accessible to a broad community of innovators.
The collaboration also reaches into applied research through the NVIDIA Accelerated Quantum Computing Research Center (NVAQC), where an NVIDIA GB200 NVL72 supercomputer can be paired with Quantinuum’s Helios to further drive hybrid quantum-GPU research, including the development of breakthrough quantum-enhanced AI applications.
A recent achievement illustrates this potential: The ADAPT-GQE framework, a transformer-based Generative Quantum AI (GenQAI) approach, uses a Generative AI model to efficiently synthesize circuits to prepare the ground state of a chemical system on a quantum computer. Developed by Quantinuum, NVIDIA, and a pharmaceutical industry leader—and leveraging NVIDIA CUDA-Q with GPU-accelerated methods—ADAPT-GQE achieved a 234x speed-up in generating training data for complex molecules. The team used the framework to explore imipramine, a molecule crucial to pharmaceutical development. The transformer was trained on imipramine conformers to synthesize ground state circuits at orders of magnitude faster than ADAPT-VQE, and the circuit produced by the transformer was run on Helios to prepare the ground state using InQuanto, Quantinuum's computational chemistry platform.
From collaborating on hardware and software integrations to GenQAI applications, the collaboration between Quantinuum and NVIDIA is building the bridge between classical and quantum computing and creating a future where AI becomes more expansive through quantum computing, and quantum computing becomes more powerful through AI.
By Dr. Noah Berthusen
The earliest works on quantum error correction showed that by combining many noisy physical qubits into a complex entangled state called a "logical qubit," this state could survive for arbitrarily long times. QEC researchers devote much effort to hunt for codes that function well as "quantum memories," as they are called. Many promising code families have been found, but this is only half of the story.
Being able to keep a qubit around for a long time is one thing, but to realize the theoretical advantages of quantum computing we need to run quantum circuits. And to make sure noise doesn't ruin our computation, these circuits need to be run on the logical qubits of our code. This is often much more challenging than performing gates on the physical qubits of our device, as these "logical gates" often require many physical operations in their implementation. What's more, it often is not immediately obvious which logical gates a code has, and so converting a physical circuit into a logical circuit can be rather difficult.
Some codes, like the famous surface code, are good quantum memories and also have easy logical gates. The drawback is that the ratio of physical qubits to logical qubits (the "encoding rate") is low, and so many physical qubits are required to implement large logical algorithms. High-rate codes that are good quantum memories have also been found, but computing on them is much more difficult. The holy grail of QEC, so to speak, would be a high-rate code that is a good quantum memory and also has easy logical gates. Here, we make progress on that front by developing a new code with those properties.
Building on prior error correcting codes
A recent work from Quantinuum QEC researchers introduced genon codes. The underlying construction method for these codes, called the "symplectic double cover," also provided a way to obtain logical gates that are well suited for Quantinuum's QCCD architecture. Namely, these "SWAP-transversal" gates are performed by applying single qubit operations and relabeling the physical qubits of the device. Thanks to the all-to-all connectivity facilitated through qubit movement on the QCCD architecture, this relabeling can be done in software essentially for free. Combined with extremely high fidelity (~1.2 x10-5) single-qubit operations, the resulting logical gates are similarly high fidelity.
Given the promise of these codes, we take them a step further in our new paper. We combine the symplectic double codes with the [[4,2,2]] Iceberg code using a procedure called "code concatenation". A concatenated code is a bit like nesting dolls, with an outer code containing codes within it---with these too potentially containing codes. More technically, in a concatenated code the logical qubits of one code act as the physical qubits of another code.
The new codes, which we call "concatenated symplectic double codes", were designed in such a way that they have many of these easily-implementable SWAP-transversal gates. Central to its construction, we show how the concatenation method allows us to "upgrade" logical gates in terms of their ease of implementation; this procedure may provide insights for constructing other codes with convenient logical gates. Notably, the SWAP-transversal gate set on this code is so powerful that only two additional operations (logical T and S) are necessary for universal computation. Furthermore, these codes have many logical qubits, and we also present numerical evidence to suggest that they are good quantum memories.
Concatenated symplectic double codes have one of the easiest logical computation schemes, and we didn’t have to sacrifice rate to achieve it. Looking forward in our roadmap, we are targeting hundreds of logical qubits at ~ 1x 10-8 logical error rate by 2029. These codes put us in a prime position to leverage the best characteristics of our hardware and create a device that can achieve real commercial advantage.
Every year, the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC) brings together the global supercomputing community to explore the technologies driving the future of computing.
Join Quantinuum at this year’s conference, taking place November 16th – 21st in St. Louis, Missouri, where we will showcase how our quantum hardware, software, and partnerships are helping define the next era of high-performance and quantum computing.
Visit Quantinuum in the Expo Hall
The Quantinuum team will be on-site at booth #4432 to showcase how we’re building the bridge between HPC and quantum.
- Live demo unit of our quantum hardware
- Our new Helios replica, providing an up-close look at the design behind our next-generation system
- The Helios chip, highlighting the innovation driving the world’s most advanced trapped-ion quantum computers
On Tuesday and Wednesday, our quantum computing experts will host daily tutorials at our booth on Helios, our next-generation hardware platform, Nexus, our all-in-one quantum computing platform, and Hybrid Workflows, featuring the integration of NVIDIA CUDA-Q with Quantinuum Systems.
Speaking Sessions at SC25
Join our team as they share insights on the opportunities and challenges of quantum integration within the HPC ecosystem:
Panel Session: The Quantum Era of HPC: Roadmaps, Challenges and Opportunities in Navigating the Integration Frontier
November 19th | 10:30 – 12:00pm CST
During this panel session, Kentaro Yamamoto from Quantinuum, will join experts from Lawrence Berkeley National Laboratory, IBM, QuEra, RIKEN, and Pawsey Supercomputing Research Centre to explore how quantum and classical systems are being brought together to accelerate scientific discovery and industrial innovation.
BoF Session: Bridging the Gap: Making Quantum-Classical Hybridization Work in HPC
November 19th | 5:15 – 6:45pm CST
Quantum-classical hybrid computing is moving from theory to reality, yet no clear roadmap exists for how best to integrate quantum processing units (QPUs) into established HPC environments. In this Birds of a Feather discussion, co-led by Quantinuum’s Grahame Vittorini and representatives from BCS, DOE, EPCC, Inria, ORNL NVIDIA, and RIKEN we hope to bring together a global community of HPC practitioners, system architects, quantum computing specialists and workflow researchers, including participants in the Workflow Community Initiative, to assess the state of hybrid integration and identify practical steps toward scalable, impactful deployment.