How we equip our users to unlock the full potential of H-Series Quantum Computers
Quantinuum is proud to introduce three new tools to help enterprise and academic users to make full use of the world-leading capabilities of the System Model H1 and H2 quantum computers
In a series of recent technical papers, Quantinuum researchers demonstrated the world-leading capabilities of the latest H-Series quantum computers, and the features and tools that make these accessible to our global customers and users.
Our teams used the H-Series quantum computers to directly measure and control non-abelian topological states of matter [1] for the first time, explore new ways to solve combinatorial optimization problems more efficiently [2], simulate molecular systems using logical qubits with error detection [3], probe critical states of matter [4], as well as exhaustively benchmark our very latest system [5].
Part of what makes such rapid technical and scientific progress possible is the effort our teams continually make to develop and improve workflow tools, helping our users to achieve successful results. In this blog post, we will explore the capabilities of three new tools in some detail, discuss their significance, and highlight their impact in recent quantum computing research.
Leakage Detection Gadget in pyTKET
“Leakage” is a quantum error process where a qubit ends up in a state outside the computational subspace and can significantly impact quantum computations. To address this issue, Quantinuum has developed a leakage detection gadget in pyTKET, a python module for interfacing with TKET, our quantum computing toolkit and optimizing compiler. This gadget, presented at the 2022 IEEE International Conference [6], acts as an error detection technique: it detects and excludes results affected by leakage, minimizing its impact on computations. It is also a valuable tool for measuring single-qubit and two-qubit spontaneous emission rates. H-Series users can access this open-source gadget through pyTKET, and an example notebook is available on the pyTKET GitHub repository.
Mid-Circuit Measurement and Qubit Reuse (MCMR) Package
The MCMR package, built as a pyTKET compiler pass, is designed to reduce the number of qubits required for executing many types of quantum algorithms, expanding the scope of what is possible on the current-generation H-Series quantum computers.
As an example, in a recent paper [4], Quantinuum researchers applied this tool to simulate the transverse-field Ising model and used only 20 qubits to simulate a much larger 128 site system (there is more detail below on this work). By measuring qubits early in the circuit, resetting them, and reusing them elsewhere, the package ingests a raw circuit and outputs an optimized circuit that requires fewer quantum resources. Previously, a scientific paper [7] and blog post on MCMR were published highlighting its benefits and applications. H-Series customers can download this package via the Quantinuum user portal.
Quantinuum H2-1 Emulator Release
To enable efficient use of Quantinuum’s 2nd generation processor, the System Model H2, Quantinuum has released the H2-1 emulator to give users greater flexibility with noise-informed state vector emulation. This emulator uses the NVIDIA's cuQuantum SDK to accelerate quantum computing simulation workflows, nearly approaching the limit of full state emulation on conventional classical hardware. The emulator is a faithful representation of the QPU it emulates. This is accomplished by not only using realistic noise models and noise parameters, but also by sharing the same software stack between the QPU and the emulator up until the job is either routed to the QPU or the classical computing processors. Most notable is that the emulator and the QPU use the same compiler allowing subtle and time-dependent errors to be appropriately represented. The H2-1 emulator was initially released as a beta product alongside the System Model H2 quantum computer at launch. It runs on a GPU backend and an upgraded global framework now offering features such as job chunking, incremental resource distribution, mid-execution job cancellation, and partial result return. Detailed information about the emulator can be found in the H2 emulator product datasheet on the Quantinuum website. H-Series customers with an H2 subscription can access the H2-1 emulator via an API or the Microsoft Azure platform.
Enabling Recent Works
Quantinuum's new enabling tools have already demonstrated their efficacy and value in recent quantum computing research, playing a vital role in advancing the field and achieving groundbreaking results. Let's expand on some notable recent examples.
All works presented here benefited from having access to our H-Series emulators; of these two significant demonstrations were the “Creation of Non-Abelian Topological Order and Anyons on a Trapped-Ion Processor” [1] and “Demonstration of improved 1-layer QAOA with Instantaneous Quantum Polynomial” [2]. These demonstrations involved extensive testing, debugging, and experiment design, for which the versatility of the H2-1 emulator proved invaluable, providing initial performance benchmarks in a realistic noisy environment. Researchers relied on the emulator's results to gauge algorithmic performance and make necessary adjustments. By leveraging the emulator's capabilities, researchers were able to accelerate their progress.
The MCMR package was extensively used in benchmarking the System Model H2 quantum computer’s world-leading capabilities [5]. Two application-level benchmarks performed in this work, approximating the solution to a MaxCut combinatorics problem using the quantum approximate optimization algorithm (QAOA) and accurately simulating a quantum dynamics model using a holographic quantum dynamics (HoloQUADS) algorithm, would have been too large to encode on H2's 32 qubits without the MCMR package. Further illustrating the overall value of these tools, in the HoloQUADS benchmark, there is a "bond qubit" that is particularly susceptible to errors due to leakage. The leakage detection gadget was used on this "bond qubit" at the end of the circuit, and any shots with a detected leakage error were discarded. The leakage detection gadget was also used to obtain the rate of leakage error per single-qubit and two-qubit gates, two component-level benchmarks.
In another scientific work [4], the MCMR compilation tool proved instrumental to simulating a transverse-field Ising model on 128 sites, using 20 qubits. With the MCMR package and by leveraging a state-of-the-art classical tensor-network ansatz expressed as a quantum circuit, the Quantinuum team was able to express the highly entangled ground state of the critical Ising model. The team showed that with H1-1's 20 qubits, the properties of this state could be measured on a 128-site system with very high fidelity, enabling a quantitatively accurate extraction of some critical properties of the model.
Key Takeaways
At Quantinuum, we are entirely devoted to producing a quantum hardware, middleware and software stack that leads the world on the most important benchmarks and includes features and tools that provide breakthrough benefit to our growing base of users. In today's NISQ hardware, "benefit" usually takes the form of getting the most performance out of today’s hardware, continually pushing what is considered to be possible. In this blog we describe two examples: error detection and discard using the “leakage detection gadget” and an automated method for circuit optimization for qubit reuse. “Benefit” can also take other forms, such as productivity. Our emulator brings many benefits to our users, but one that resonates the most is productivity. Being a faithful representation of our QPU performance, the emulator is an accessible tool which users have at their disposal to develop and test new, innovative algorithms. The tools and features Quantinuum releases are driven by users’ feedback; whether you are new to H-Series or a seasoned user, please reach-out and let us know how we can help bring benefit to your research and use case.
Footnotes:
[1] Mohsin Iqbal et al., Creation of Non-Abelian Topological Order and Anyons on a Trapped-Ion Processor (2023), arXiv:2305.03766 [quant-ph]
[2] Sebastian Leontica and David Amaro, Exploring the neighborhood of 1-layer QAOA with Instantaneous Quantum Polynomial circuits (2022), arXiv:2210.05526 [quant-ph]
[3] Kentaro Yamamoto, Samuel Duffield, Yuta Kikuchi, and David Muñoz Ramo, Demonstrating Bayesian Quantum Phase Estimation with Quantum Error Detection (2023), arXiv:2306.16608 [quant-ph]
[4] Reza Haghshenas, et al., Probing critical states of matter on a digital quantum computer (2023),
arXiv:2305.01650 [quant-ph]
[5] S. A. Moses, et al., A Race Track Trapped-Ion Quantum Processor (2023), arXiv:2305.03828 [quant-ph]
[6] K. Mayer, Mitigating qubit leakage errors in quantum circuits with gadgets and post-selection, 2022 IEEE International Conference on Quantum Computing and Engineering (QCE), Broomfield, CO, USA, (2022), pp. 809-809, doi: 10.1109/QCE53715.2022.00126.
[7] Matthew DeCross, Eli Chertkov, Megan Kohagen, and Michael Foss-Feig, Qubit-reuse compilation with mid-circuit measurement and reset (2022), arXiv:2210.08039 [quant-ph]
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.