Quantinuum’s H-Series hits 56 physical qubits that are all-to-all connected, and departs the era of classical simulation
In collaboration with JPMorgan Chase & Co., Quantinuum’s H2-1 achieved a massive uplift in an iconic demonstration
The first half of 2024 will go down as the period when we shed the last vestiges of the “wait and see” culture that has dominated the quantum computing industry. Thanks to a run of recent achievements, we have helped to lead the entire quantum computing industry into a new, post-classical era.
Today we are announcing the latest of these achievements: a major qubit count enhancement to our flagship System Model H2 quantum computer from 32 to 56 qubits. We also reveal meaningful results of work with our partner JPMorgan Chase & Co. that showcases a significant lift in performance.
But to understand the full importance of today’s announcements, it is worth recapping the succession of breakthroughs that confirm that we are entering a new era of quantum computing in which classical simulation will be infeasible.
A historic run
Between January and June 2024, Quantinuum’s pioneering teams published a succession of results that accelerate our path to universal fault-tolerant quantum computing.
Our technical teams first presented a long-sought solution to the “wiring problem”, an engineering challenge that affects all types of quantum computers. In short, most current designs will require an impossible number of wires connected to the quantum processor to scale to large qubit numbers. Our solution allows us to scale to high qubit numbers with no issues, proving that our QCCD architecture has the potential to scale.
Next, we became the first quantum computing company in the world to hit “three 9s” two qubit gate fidelity across all qubit pairs in a production device. This level of fidelity in 2-qubit gate operations was long thought to herald the point at which error corrected quantum computing could become a reality. It has accelerated and intensified our focus on quantum error correction (QEC). Our scientists and engineers are working with our customers and partners to achieve multiple breakthroughs in QEC in the coming months, many of which will be incorporated into products such as the H-Series and our chemistry simulation platform, InQuanto™.
Following that, with our long-time partner Microsoft, we hit an error correction performance threshold that many believed was still years away. The System Model H2 became the first – and only – quantum computer in the world capable of creating and computing with highly reliable logical (error corrected) qubits. In this demonstration, the H2-1 configured with 32 physical qubits supported the creation of four highly reliable logical qubits operating at “better than break-even”. In the same demonstration, we also shared that logical circuit error rates were shown to be up to 800x lower than the corresponding physical circuit error rates. No other quantum computing company is even close to matching this achievement (despite many feverish claims in the past 12 months).
Pushing to the limits of supercomputing … and beyond
The quantum computing industry is departing the era when quantum computers could be simulated by a classical computer. Today, we are making two milestone announcements. The first is that our H2-1 processor has been upgraded to 56 trapped-ion qubits, making it impossible to classically simulate, without any loss of the market-leading fidelity, all-to-all qubit connectivity, mid-circuit measurement, qubit reuse, and feed forward.
The second is that the upgrade of H2-1 from 32 to 56 qubits makes our processor capable of challenging the world’s most powerful supercomputers. This demonstration was achieved in partnership with our long-term collaborator JPMorgan Chase & Co. and researchers from Caltech and Argonne National Lab.
Our collaboration tackled a well-known algorithm, Random Circuit Sampling (RCS), and measured the quality of our results with a suite of tests including the linear cross entropy benchmark (XEB) – an approach first made famous by Google in 2019 in a bid to demonstrate “quantum supremacy”. An XEB score close to 0 says your results are noisy – and do not utilize the full potential of quantum computing. In contrast, the closer an XEB score is to 1, the more your results demonstrate the power of quantum computing. The results on H2-1 are excellent, revealing, and worth exploring in a little detail. Here is the complete data on GitHub.
Better qubits, better results
Our results show how far quantum hardware has come since Google’s initial demonstration. They originally ran circuits on 53 superconducting qubits that were deep enough to severely frustrate high-fidelity classical simulation at the time, achieving an estimated XEB score of ~0.002. While they showed that this small value was statistically inconsistent with zero, improvements in classical algorithms and hardware have steadily increased what XEB scores are achievable by classical computers, to the point that classical computers can now achieve scores similar to Google’s on their original circuits.
In contrast, we have been able to run circuits on all 56 qubits in H2-1 that are deep enough to challenge high-fidelity classical simulation while achieving an estimated XEB score of ~0.35. This >100x improvement implies the following: even for circuits large and complex enough to frustrate all known classical simulation methods, the H2 quantum computer produces results without making even a single error about 35% of the time. In contrast to past announcements associated with XEB experiments, 35% is a significant step towards the idealized 100% fidelity limit in which the computational advantage of quantum computers is clearly in sight.
This huge jump in quality is made possible by Quantinuum’s market-leading high fidelity and also our unique all-to-all connectivity. Our flexible connectivity, enabled by our QCCD architecture, enables us to implement circuits with much more complex geometries than the 2D geometries supported by superconducting-based quantum computers. This specific advantage means our quantum circuits become difficult to simulate classically with significantly fewer operations (or gates). These capabilities have an enormous impact on how our computational power scales as we add more qubits: since noisy quantum computers can only run a limited number of gates before returning unusable results, needing to run fewer gates ultimately translates into solving complex tasks with consistent and dependable accuracy.
This is a vitally important moment for companies and governments watching this space and deciding when to invest in quantum: these results underscore both the performance capabilities and the rapid rate of improvement of our processors, especially the System Model H2, as a prime candidate for achieving near-term value.
So what of the comparison between the H2-1 results and a classical supercomputer?
A direct comparison can be made between the time it took H2-1 to perform RCS and the time it took a classical supercomputer. However, classical simulations of RCS can be made faster by building a larger supercomputer (or by distributing the workload across many existing supercomputers). A more robust comparison is to consider the amount of energy that must be expended to perform RCS on either H2-1 or on classical computing hardware, which ultimately controls the real cost of performing RCS. An analysis based on the most efficient known classical algorithm for RCS and the power consumption of leading supercomputers indicates that H2-1 can perform RCS at 56 qubits with an estimated 30,000x reduction in power consumption. These early results should be seen as very attractive for data center owners and supercomputing facilities looking to add quantum computers as “accelerators” for their users.
Where we go next
Today’s milestone announcements are clear evidence that the H2-1 quantum processor can perform computational tasks with far greater efficiency than classical computers. They underpin the expectation that as our quantum computers scale beyond today’s 56 qubits to hundreds, thousands, and eventually millions of high-quality qubits, classical supercomputers will quickly fall behind. Quantinuum’s quantum computers are likely to become the device of choice as scrutiny continues to grow of the power consumption of classical computers applied to highly intensive workloads such as simulating molecules and material structures – tasks that are widely expected to be amenable to a speedup using quantum computers.
With this upgrade in our qubit count to 56, we will no longer be offering a commercial “fully encompassing” emulator – a mathematically exact simulation of our H2-1 quantum processor is now impossible, as it would take up the entire memory of the world’s best supercomputers. With 56 qubits, the only way to get exact results is to run on the actual hardware, a trend the leaders in this field have already embraced.
More generally, this work demonstrates that connectivity, fidelity, and speed are all interconnected when measuring the power of a quantum computer. Our competitive edge will persist in the long run; as we move to running more algorithms at the logical level, connectivity and fidelity will continue to play a crucial role in performance.
“We are entirely focused on the path to universal fault tolerant quantum computers. This objective has not changed, but what has changed in the past few months is clear evidence of the advances that have been made possible due to the work and the investment that has been made over many, many years. These results show that whilst the full benefits of fault tolerant quantum computers have not changed in nature, they may be reachable earlier than was originally expected, and crucially, that along the way, there will be tangible benefits to our customers in their day-to-day operations as quantum computers start to perform in ways that are not classically simulatable. We have an exciting few months ahead of us as we unveil some of the applications that will start to matter in this context with our partners across a number of sectors.”
– Ilyas Khan, Chief Product Officer
Stay tuned for results in error correction, physics, chemistry and more on our new 56-qubit processor.
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 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.
One of the greatest privileges of working directly with the world’s most powerful quantum computer at Quantinuum is building meaningful experiments that convert theory into practice. The privilege becomes even more compelling when considering that our current quantum processor – our H2 system – will soon be enhanced by Helios, a quantum computer potentially a stunning trillion times more powerful, and due for launch in just a few months. The moment has now arrived when we can build a timeline for applications that quantum computing professionals have anticipated for decades and which are experimentally supported.
Quantinuum’s applied algorithms team has released an end-to-end implementation of a quantum algorithm to solve a central problem in knot theory. Along with an efficiently verifiable benchmark for quantum processors, it allows for concrete resource estimates for quantum advantage in the near-term. The research team, included Quantinuum researchers Enrico Rinaldi, Chris Self, Eli Chertkov, Matthew DeCross, David Hayes, Brian Neyenhuis, Marcello Benedetti, and Tuomas Laakkonen of the Massachusetts Institute of Technology. In this article, Konstantinos Meichanetzidis, a team leader from Quantinuum’s AI group who led the project, writes about the problem being addressed and how the team, adopting an aggressively practical mindset, quantified the resources required for quantum advantage:
Knot theory is a field of mathematics called ‘low-dimensional topology’, with a rich history, stemming from a wild idea proposed by Lord Kelvin, who conjectured that chemical elements are different knots formed by vortices in the aether. Of course, we know today that the aether theory was falsified by the Michelson-Morley experiment, but mathematicians have been classifying, tabulating, and studying knots ever since. Regarding applications, the pure mathematics of knots can find their way into cryptography, but knot theory is also intrinsically related to many aspects of the natural sciences. For example, it naturally shows up in certain spin models in statistical mechanics, when one studies thermodynamic quantities, and the magnetohydrodynamical properties of knotted magnetic fields on the surface of the sun are an important indicator of solar activity, to name a few examples. Remarkably, physical properties of knots are important in understanding the stability of macromolecular structures. This is highlighted by work of Cozzarelli and Sumners in the 1980’s, on the topology of DNA, particularly how it forms knots and supercoils. Their interdisciplinary research helped explain how enzymes untangle and manage DNA topology, crucial for replication and transcription, laying the foundation for using mathematical models to predict and manipulate DNA behavior, with broad implications in drug development and synthetic biology. Serendipitously, this work was carried out during the same decade as Richard Feynman, David Deutsch, and Yuri Manin formed the first ideas for a quantum computer.
Most importantly for our context, knot theory has fundamental connections to quantum computation, originally outlined by Witten’s work in topological quantum field theory, concerning spacetimes without any notion of distance but only shape. In fact, this connection formed the very motivation for attempting to build topological quantum computers, where anyons – exotic quasiparticles that live in two-dimensional materials – are braided to perform quantum gates. The relation between knot theory and quantum physics is the most beautiful and bizarre facts you have never heard of.
The fundamental problem in knot theory is distinguishing knots, or more generally, links. To this end, mathematicians have defined link invariants, which serve as ‘fingerprints’ of a link. As there are many equivalent representations of the same link, an invariant, by definition, is the same for all of them. If the invariant is different for two links then they are not equivalent. The specific invariant our team focused on is the Jones polynomial.
They all have the same Jones polynomial, as it is an invariant.
The mind-blowing fact here is that any quantum computation corresponds to evaluating the Jones polynomial of some link, as shown by the works of Freedman, Larsen, Kitaev, Wang, Shor, Arad, and Aharonov. It reveals that this abstract mathematical problem is truly quantum native. In particular, the problem our team tackled was estimating the value of the Jones polynomial at the 5th root of unity. This is a well-studied case due to its relation to the infamous Fibonacci anyons, whose braiding is capable of universal quantum computation.
Building and improving on the work of Shor, Aharonov, Landau, Jones, and Kauffman, our team developed an efficient quantum algorithm that works end-to end. That is, given a link, it outputs a highly optimized quantum circuit that is readily executable on our processors and estimates the desired quantity. Furthermore, our team designed problem-tailored error detection and error mitigation strategies to achieve a higher accuracy.
In addition to providing a full pipeline for solving this problem, a major aspect of this work was to use the fact that the Jones polynomial is an invariant to introduce a benchmark for noisy quantum computers. Most importantly, this benchmark is efficiently verifiable, a rare property since for most applications, exponentially costly classical computations are necessary for verification. Given a link whose Jones polynomial is known, the benchmark constructs a large set of topologically equivalent links of varying sizes. In turn, these result in a set of circuits of varying numbers of qubits and gates, all of which should return the same answer. Thus, one can characterize the effect of noise present in a given quantum computer by quantifying the deviation of its output from the known result.
The benchmark introduced in this work allows one to identify the link sizes for which there is exponential quantum advantage in terms of time to solution against the state-of-the-art classical methods. These resource estimates indicate our next processor, Helios, with 96 qubits and at least 99.95% two-qubit gate-fidelity, is extremely close to meeting these requirements. Furthermore, Quantinuum’s hardware roadmap includes even more powerful machines that will come online by the end of the decade. Notably, an advantage in energy consumption emerges for even smaller link sizes. Meanwhile, our teams aim to continue reducing errors through improvements in both hardware and software, thereby moving deeper into quantum advantage territory.
The importance of this work, indeed the uniqueness of this work in the quantum computing sector, is its practical end-to-end approach. The advantage-hunting strategies introduced are transferable to other “quantum-easy classically-hard” problems. Our team’s efforts motivate shifting the focus toward specific problem instances rather than broad problem classes, promoting an engineering-oriented approach to identifying quantum advantage. This involves first carefully considering how quantum advantage should be defined and quantified, thereby setting a high standard for quantum advantage in scientific and mathematical domains. And thus, making sure we instill confidence in our customers and partners.
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