A Quantinuum-led team has built the quantum programming tools for real-time magic state distillation on a quantum computer
Researchers at Quantinuum and Microsoft’s Azure Quantum used the Quantum Intermediate Representation (QIR) to demonstrate a magic state distillation protocol for the first time on quantum hardware – a key element necessary for universal, fault-tolerant quantum computing
Building a quantum computer that offers advantages over classical computers is the goal of quantum computing groups worldwide. A competitive quantum computer must be “universal”, requiring the ability to perform all operations already possible on a classical computer, as well as new ones specific to quantum computing. Of course, that’s just the beginning – it should also be able to do this in a reasonable amount of time, to deal effectively with noise from the environment, and to perform computations to arbitrary accuracy.
This is a lot to get right, and over the years quantum computer scientists have described ways to solve these often-overlapping challenges. To deal with noise from the environment and achieve arbitrary accuracy, quantum computers need to be able to keep going even as noise accumulates on the quantum bits, or qubits, which hold the quantum information. Such fault-tolerance may be achieved using quantum error correction, where ensembles of physical qubits are encoded into logical qubits and those are used to counteract noise and perform computational operations called gates. Unfortunately, no single quantum error correction code plays well with the goal of universality because all codes lack a complete universal set of fault-tolerant gates (the technical reason for this comes down to the way quantum gates are executed between logical qubits – the native gate set on error-corrected logical qubits are known by experts as transversal gates, and they do not include all the gates needed for universal quantum computing).
The solution to this obstacle to universality is a magic state, a quantum state which provides for the missing gate when error correcting codes are used. High fidelity magic states are achieved by a process of distillation, which purifies them from other noisier magic states. It is widely recognized that magic state distillation is one of the totemic challenges on the path towards universal, fault-tolerant quantum computing. Quantinuum’s scientists, in close collaboration with a team at Microsoft, set out to demonstrate the distillation process in real-time using physical qubits on a quantum computer for the first time.
The results of this work are available in a new paper, Advances in compilation for quantum hardware -- A demonstration of magic state distillation and repeat until success protocols.
Magic state distillation
How does magic state distillation work? Imagine a factory, taking in many qubits in imperfect initial states at one end. Broadly speaking, the factory distills the imperfect states into an almost pure state with a smaller error probability, by sending them through a well-defined process over and over. In this case, the process takes in a group of five qubits. It applies a quantum error correcting code that entangles these five qubits, with four used to test whether the fifth, target qubit has been purified. If the process fails, the ensemble is discarded and the process repeats. If it succeeds, the newly distilled target qubit is kept and combined with four other successes to form a new ensemble, which then rejoins the process of continued purification. By undertaking this process many times, the purity of the magic state increases at each step, gradually moving towards the conditions required for universal, fault-tolerant quantum computing.
Despite being the subject of theoretical exploration over decades, real-time magic state distillation had never been realized on a quantum computer. In typical pioneering style, the Quantinuum and Microsoft team decided to take on this challenge. But before they could get started, they recognized that their toolset would have to be significantly sharpened up.
Creating new tools for quantum programming
At the heart of magic state distillation is a highly complex repeating process, which requires state-of-the-art protocols and control flow logic built on a best-in-class programming toolset. The research team turned to Quantum Intermediate Representation (QIR) to simplify and streamline the programming of this complex quantum computing process.
QIR is a is a quantum-specific code representation based on the popular open-sourced classical LLVM intermediate language, with the addition of structures and protocols that support the maturation and modernization of quantum computing. QIR includes elements that are essential in classical computing, but which are yet to be standardized in quantum computing, such as the humble programming loop.
Loops, which often take forms like "for...next" or "do...while," are central to programming, allowing code to repeat instructions in a stepwise manner until a condition is met. In quantum computing, this is a tough challenge because loops require control flow logic and mid-circuit measurement, which are difficult to realize in a quantum computer but have been demonstrated in Quantinuum’s System Model H1-1, Powered by Honeywell. Loops are essential for realizing magic state distillation and it’s well-understood that LLVM is great at optimizing complex control flow, including loops. This made magic state distillation a natural choice for demonstrating a valuable application of QIR and making for a great example of the use of a classical technique in a quantum context.
Result: demonstrating a magic state distillation protocol
The team used Quantinuum’s H1-1 quantum computer – benefiting from industry-leading components such as mid-circuit measurement, qubit reuse and feed-forward – to make possible the quantum looping required for a magic state distillation protocol, and becoming the first quantum computing team ever to run a real-time magic state distillation protocol on quantum hardware.
Four ways to achieve a quantum computer programmable loop
Building on this success, the team designed further experiments to assess the potential of four methods for exploring the use of a quantum protocol called a repeat-until-success (RUS) circuit to achieve a loop process. First, they hard-coded a loop directly into the extended OpenQASM 2.0, a widely used quantum assembly language, but which requires additional overhead to target advanced components on Quantinuum's very versatile H-Series quantum computer. Against this, they compared two alternative methods for coding a loop in a standard high-level programming language: controlled recursion, which was directed through both OpenQASM and through QIR; and a native for loop made possible within QIR.
The results were clear-cut: the hard-coded OpenQASM 2.0 loop performed as well as the theoretical prediction, maintaining high quality results after a number of loops, as did the natively-coded QIR for loop. The two recursive loops saw the quality of their results drop away fast as the loop limit was raised. But in a head-to-head between hard-coded OpenQASM and QIR, which converts high-level source code from many prominent and familiar languages into low-level machine code, QIR won hands-down on the basis of practicality.
Martin Roetteler, Director of Quantum Applications at Microsoft, shared: “This was a very exciting exploration of control flow logic on quantum hardware. In seeking to understand the capabilities of QIR to optimize programming structures on real hardware, we were rewarded with a clear answer, and an important demonstration of the capabilities of QIR.”
H2’s 32 qubits will power the next phase
In follow-up work, the team is now preparing to run a logical magic state protocol on the H2-1 quantum computer with its 32 high-fidelity qubits, and hopes to become the first group to successfully achieve logical magic state distillation. The features and fidelity offered by the H2 make it one of the best quantum computers currently capable of shooting for such a major milestone on the journey towards fault tolerance, while the current work demonstrates that, in QIR, the necessary control flow logic is now available to achieve it.
The paper discussed in this post was authored by Natalie C. Brown, John P. Campora III, Cassandra Granade, Bettina Heim, Stefan Wernli, Ciaran Ryan-Anderson, Dominic Lucchetti, Adam Paetznick, Martin Roetteler, Krysta Svore and Alex Chernoguzov.
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.
The most common question in the public discourse around quantum computers has been, “When will they be useful?” We have an answer.
Very recently in Nature we announced a successful demonstration of a quantum computer generating certifiable randomness, a critical underpinning of our modern digital infrastructure. We explained how we will be taking a product to market this year, based on that advance – one that could only be achieved because we have the world’s most powerful quantum computer.
Today, we have made another huge leap in a different domain, providing fresh evidence that our quantum computers are the best in the world. In this case, we have shown that our quantum computers can be a useful tool for advancing scientific discovery.
Understanding magnetism
Our latest paper shows how our quantum computer rivals the best classical approaches in expanding our understanding of magnetism. This provides an entry point that could lead directly to innovations in fields from biochemistry, to defense, to new materials. These are tangible and meaningful advances that will deliver real world impact.
To achieve this, we partnered with researchers from Caltech, Fermioniq, EPFL, and the Technical University of Munich. The team used Quantinuum’s System Model H2 to simulate quantum magnetism at a scale and level of accuracy that pushes the boundaries of what we know to be possible.
As the authors of the paper state:
“We believe the quantum data provided by System Model H2 should be regarded as complementary to classical numerical methods, and is arguably the most convincing standard to which they should be compared.”
Our computer simulated the quantum Ising model, a model for quantum magnetism that describes a set of magnets (physicists call them ‘spins’) on a lattice that can point up or down, and prefer to point the same way as their neighbors. The model is inherently “quantum” because the spins can move between up and down configurations by a process known as “quantum tunneling”.
Gaining material insights
Researchers have struggled to simulate the dynamics of the Ising model at larger scales due to the enormous computational cost of doing so. Nobel laureate physicist Richard Feynman, who is widely considered to be the progenitor of quantum computing, once said, “it is impossible to represent the results of quantum mechanics with a classical universal device.” When attempting to simulate quantum systems at comparable scales on classical computers, the computational demands can quickly become overwhelming. It is the inherent ‘quantumness’ of these problems that makes them so hard classically, and conversely, so well-suited for quantum computing.
These inherently quantum problems also lie at the heart of many complex and useful material properties. The quantum Ising model is an entry point to confront some of the deepest mysteries in the study of interacting quantum magnets. While rooted in fundamental physics, its relevance extends to wide-ranging commercial and defense applications, including medical test equipment, quantum sensors, and the study of exotic states of matter like superconductivity.
Instead of tailored demonstrations that claim ‘quantum advantage’ in contrived scenarios, our breakthroughs announced this week prove that we can tackle complex, meaningful scientific questions difficult for classical methods to address. In the work described in this paper, we have proved that quantum computing could be the gold standard for materials simulations. These developments are critical steps toward realizing the potential of quantum computers.
With only 56 qubits in our commercially available System Model H2, the most powerful quantum system in the world today, we are already testing the limits of classical methods, and in some cases, exceeding them. Later this year, we will introduce our massively more powerful 96-qubit Helios system - breaching the boundaries of what until recently was deemed possible.
The marriage of AI and quantum computing is going to have a widespread and meaningful impact in many aspects of our lives, combining the strengths of both fields to tackle complex problems.
Quantum and AI are the ideal partners. At Quantinuum, we are developing tools to accelerate AI with quantum computers, and quantum computers with AI. According to recent independent analysis, our quantum computers are the world’s most powerful, enabling state-of-the-art approaches like Generative Quantum AI (Gen QAI), where we train classical AI models with data generated from a quantum computer.
We harness AI methods to accelerate the development and performance of our full quantum computing stack as opposed to simply theorizing from the sidelines. A paper in Nature Machine Intelligence reveals the results of a recent collaboration between Quantinuum and Google DeepMind to tackle the hard problem of quantum compilation.
The work shows a classical AI model supporting quantum computing by demonstrating its potential for quantum circuit optimization. An AI approach like this has the potential to lead to more effective control at the hardware level, to a richer suite of middleware tools for quantum circuit compilation, error mitigation and correction, even to novel high-level quantum software primitives and quantum algorithms.
An AI power-up for circuit optimization
The joint Quantinuum-Google DeepMind team of researchers tackled one of quantum computing’s most pressing challenges: minimizing the number of highly expensive but essential T-gates required for universal quantum computation. This is important specifically for the fault-tolerant regime, which is becoming increasingly relevant as quantum error correction protocols are being explored on rapidly developing quantum hardware. The joint team of researchers adapted AlphaTensor, Google DeepMind’s reinforcement learning AI system for algorithm discovery, which was introduced to improve the efficiency of linear algebra computations. The team introduced AlphaTensor-Quantum, which takes as input a quantum circuit and returns a new, more efficient one in terms of number of T-gates, with exactly the same functionality!
AlphaTensor-Quantum outperformed current state-of-the art optimization methods and matched the best human-designed solutions across multiple circuits in a thoroughly curated set of circuits, chosen for their prevalence in many applications, from quantum arithmetic to quantum chemistry. This breakthrough shows the potential for AI to automate the process of finding the most efficient quantum circuit. This is the first time that such an AI model has been put to the problem of T-count reduction at such a large scale.
A quantum power-up for machine learning
The symbiotic relationship between quantum and AI works both ways. When AI and quantum computing work together, quantum computers could dramatically accelerate machine learning algorithms, whether by the development and application of natively quantum algorithms, or by offering quantum-generated training data that can be used to train a classical AI model.
Our recent announcement about Generative Quantum AI (Gen QAI) spells out our commitment to unlocking the value of the data generated by our H2 quantum computer. This value arises from the world’s leading fidelity and computational power of our System Model H2, making it impossible to exactly simulate on any classical computer, and therefore the data it generates – that we can use to train AI – is inaccessible by any other means. Quantinuum’s Chief Scientist for Algorithms and Innovation, Prof. Harry Buhrman, has likened accessing the first truly quantum-generated training data to the invention of the modern microscope in the seventeenth century, which revealed an entirely new world of tiny organisms thriving unseen within a single drop of water.
Recently, we announced a wide-ranging partnership with NVIDIA. It charts a course to commercial scale applications arising from the partnership between high-performance classical computers, powerful AI systems, and quantum computers that breach the boundaries of what previously could and could not be done. Our President & CEO, Dr. Raj Hazra spoke to CNBC recently about our partnership. Watch the video here.
As we prepare for the next stage of quantum processor development, with the launch of our Helios system in 2025, we’re excited to see how AI can help write more efficient code for quantum computers – and how our quantum processors, the most powerful in the world, can provide a backend for AI computations.
As in any truly symbiotic relationship, the addition of AI to quantum computing equally benefits both sides of the equation.
To read more about Quantinuum and Google DeepMind’s collaboration, please read the scientific paper here.
Few things are more important to the smooth functioning of our digital economies than trustworthy security. From finance to healthcare, from government to defense, quantum computers provide a means of building trust in a secure future.
Quantinuum and its partners JPMorganChase, Oak Ridge National Laboratory, Argonne National Laboratory and the University of Texas used quantum computers to solve a known industry challenge, generating the “random seeds” that are essential for the cryptography behind all types of secure communication. As our partner and collaborator, JPMorganChase explain in this blog post that true randomness is a scarce and valuable commodity.
This year, Quantinuum will introduce a new product based on this development that has long been anticipated, but until now thought to be some years away from reality.
It represents a major milestone for quantum computing that will reshape commercial technology and cybersecurity: Solving a critical industry challenge by successfully generating certifiable randomness.
Building on the extraordinary computational capabilities of Quantinuum’s H2 System – the highest-performing quantum computer in the world – our team has implemented a groundbreaking approach that is ready-made for industrial adoption. Nature today reported the results of a proof of concept with JPMorganChase, Oak Ridge National Laboratory, Argonne National Laboratory, and the University of Texas alongside Quantinuum. It lays out a new quantum path to enhanced security that can provide early benefits for applications in cryptography, fairness, and privacy.
By harnessing the powerful properties of quantum mechanics, we’ve shown how to generate the truly random seeds critical to secure electronic communication, establishing a practical use-case that was unattainable before the fidelity and scalability of the H2 quantum computer made it reliable. So reliable, in fact, that it is now possible to turn this into a commercial product.
Quantinuum will integrate quantum-generated certifiable randomness into our commercial portfolio later this year. Alongside Generative Quantum AI and our upcoming Helios system – capable of tackling problems a trillion times more computationally complex than H2 – Quantinuum is further cementing its leadership in the rapidly-advancing quantum computing industry.
This Matters Because Cybersecurity Matters
Cryptographic security, a bedrock of the modern economy, relies on two essential ingredients: standardized algorithms and reliable sources of randomness – the stronger the better. Non-deterministic physical processes, such as those governed by quantum mechanics, are ideal sources of randomness, offering near-total unpredictability and therefore, the highest cryptographic protection. Google, when it originally announced it had achieved quantum supremacy, speculated on the possibility of using the random circuit sampling (RCS) protocol for the commercial production of certifiable random numbers. RCS has been used ever since to demonstrate the performance of quantum computers, including a milestone achievement in June 2024 by Quantinuum and JPMorganChase, demonstrating their first quantum computer to defy classical simulation. More recently RCS was used again by Google for the launch of its Willow processor.
In today’s announcement, our joint team used the world’s highest-performing quantum and classical computers to generate certified randomness via RCS. The work was based on advanced research by Shih-Han Hung and Scott Aaronson of the University of Texas at Austin, who are co-authors on today’s paper.
Following a string of major advances in 2024 – solving the scaling challenge, breaking new records for reliability in partnership with Microsoft, and unveiling a hardware roadmap, today proves how quantum technology is capable of creating tangible business value beyond what is available with classical supercomputers alone.
What follows is intended as a non-technical explainer of the results in today’s Nature paper.
Certified Randomness: The First Commercial Application for Quantum Computers
For security sensitive applications, classical random number generation is unsuitable because it is not fundamentally random and there is a risk it can be “cracked”. The holy grail is randomness whose source is truly unpredictable, and Nature provides just the solution: quantum mechanics. Randomness is built into the bones of quantum mechanics, where determinism is thrown out the door and outcomes can be true coin flips.
At Quantinuum, we have a strong track record in developing methods for generating certifiable randomness using a quantum computer. In 2021, we introduced Quantum Origin to the market, as a quantum-generated source of entropy targeted at hardening classically-generated encryption keys, using well known quantum technologies that prior to that it had not been possible to use.
In their theory paper, “Certified Randomness from Quantum Supremacy”, Hung and Aaronson ask the question: is it possible to repurpose RCS, and use it to build an application that moves beyond quantum technologies and takes advantage of the power of a quantum computer running quantum circuits?
This was the inspiration for the collaboration team led by JPMorganChase and Quantinuum to draw up plans to execute the proposal using real-world technology. Here’s how it worked:
- The team sent random circuits to Quantinuum’s H2, the world’s highest performing commercially available quantum computer.
- The quantum computer executed each circuit and returned the corresponding sample. The response times were remarkably short, and it could be proven that the circuits could not have been simulated classically within those times, even using the best-known techniques on computing resources greater than those available in the world’s most powerful classical supercomputer.
- The randomness of the returned sample was mathematically certified using Frontier, the world’s most powerful classical supercomputer, establishing it achieved a “passing threshold” on a measure known as the “cross-entropy benchmark”. The better your quantum computer, the higher you can set the “passing threshold”. When the threshold is sufficiently high, "spoofing" the cross-entropy benchmark using only classical methods becomes inefficient.
- Therefore, if the samples are returned quickly and meet the high threshold, the team could be confident that they were generated by a quantum computer – and thus be truly random.
This confirmed that Quantinuum’s quantum computer is not only incapable of being matched by classical computers but can also be used reliably to produce a certifiably random seed from a quantum computer without the need to build your own device, or even trust the device you are accessing.
Looking ahead
The use of randomness in critical cybersecurity environments will gravitate towards quantum resources, as the security demands of end users grows in the face of ongoing cyber threats.
The era of quantum utility offers the promise of radical new approaches to solving substantial and hard problems for businesses and governments.
Quantinuum’s H2 has now demonstrated practical value for cybersecurity vendors and customers alike, where non-deterministic sources of encryption may in time be overtaken by nature’s own source of randomness.
In 2025, we will launch our Helios device, capable of supporting at least 50 high-fidelity logical qubits – and further extending our lead in the quantum computing sector. We thus continue our track record of disclosing our objectives and then meeting or surpassing them. This commitment is essential, as it generates faith and conviction among our partners and collaborators, that empirical results such as those reported today can lead to successful commercial applications.
Helios, which is already in its late testing phase, ahead of being commercially available later this year, brings higher fidelity, greater scale, and greater reliability. It promises to bring a wider set of hybrid quantum-supercomputing opportunities to our customers – making quantum computing more valuable and more accessible than ever before.
And in 2025 we look forward to adding yet another product, building out our cybersecurity portfolio with a quantum source of certifiably random seeds for a wide range of customers who require this foundational element to protect their businesses and organizations.