We’re working on bringing the power of quantum computing – and quantum machine learning – to particle physics
Particle accelerators like the LHC take serious computing power. Often on the bleeding-edge of computing technology, accelerator projects sometimes even drive innovations in computing. In fact, while there is some controversy over exactly where the world wide web was created, it is often attributed to Tim Berners-Lee at CERN, who developed it to meet the demand for automated information-sharing between scientists in universities and institutes around the world.
With annual data generated by accelerators in excess of exabytes (a billion gigabytes), tens of millions of lines of code written to support the experiments, and incredibly demanding hardware requirements, it’s no surprise that the High Energy Physics community is interested in quantum computing, which offers real solutions to some of their hardest problems. Furthermore, the HEP community is well-positioned to support the early stages of technological development: with budgets in the 10s of billions per year and tens of thousands of scientists and engineers working on accelerator and computational physics, this is a ripe industry for quantum computing to tap.
As the authors of this paper stated: “[Quantum Computing] encompasses several defining characteristics that are of particular interest to experimental HEP: the potential for quantum speed-up in processing time, sensitivity to sources of correlations in data, and increased expressivity of quantum systems... Experiments running on high-luminosity accelerators need faster algorithms; identification and reconstruction algorithms need to capture correlations in signals; simulation and inference tools need to express and calculate functions that are classically intractable”
The authors go on to state: “Within the existing data reconstruction and analysis paradigm, access to algorithms that exhibit quantum speed-ups would revolutionize the simulation of large-scale quantum systems and the processing of data from complex experimental set-ups. This would enable a new generation of precision measurements to probe deeper into the nature of the universe. Existing measurements may contain the signatures of underlying quantum correlations or other sources of new physics that are inaccessible to classical analysis techniques. Quantum algorithms that leverage these properties could potentially extract more information from a given dataset than classical algorithms.”
Our scientists have been working with a team at DESY, one of the world’s leading accelerator centers, to bring the power of quantum computing to particle physics. DESY, short for Deutsches Elektronen-Synchrotron, is a national research center for fundamental science located in Hamburg and Zeuthen, where the Center for Quantum Technologies and Applications (CQTA) is based. DESY operates, develops, and constructs particle accelerators used to investigate the structure, dynamics and function of matter, and conducts a broad spectrum of interdisciplinary scientific research. DESY employs about 3,000 staff members from more than 60 nations, and is part of the worldwide computer network to store and analyze the enormous flood of data that is produced by the LHC in Geneva.
In a recent paper, our scientists collaborated with scientists from DESY, the Leiden Institute of Advanced Computer Science (LIACS), and Northeastern University to explore using a generative quantum machine learning model, called a “quantum Boltzmann machine” to untangle data from CERN’s LHC.
The goal was to learn probability distributions relevant to high energy physics better than the corresponding classical models. The data specifically contains “particle jet events”, which describe how colliders collect data about the subatomic particles generated during the experiments.
In some cases the quantum Boltzmann machine was indeed better, compared to a classical Boltzmann machine. The team is analyzed when and why this happens, understanding better how to apply these new quantum tools in this research setting. The team also studied the effect of the data encoding into a quantum state, noting that it can have a decisive effect on the training performance. Especially enticing is that the quantum Boltzmann machine is efficiently trainable, which our scientists showed in a recent paper published in Nature Communications Physics.
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.
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
From Monday through 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
- 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.
Quantinuum’s real world experiment, on the world’s most powerful quantum computer, is the largest of its kind— so large that no amount of classical computing could match it
In 1911, a student working under famed physicist Heike Kamerlingh Onnes made a discovery that would rewire our understanding of electricity. The student was studying the electrical resistance of wires, a seemingly simple question that held secrets destined to surprise the world.
Kamerlingh Onnes had recently succeeded in liquefying helium, a feat so impressive it earned him the Nobel Prize in Physics two years later. With this breakthrough, scientists could now immerse other materials in a cold bath of liquid Helium, cooling things to unprecedented temperatures and observing their behavior.
Many theories existed about what would happen to a wire at such low temperatures. Lord Kelvin predicted that electrons would freeze in place, making the resistance infinite and stopping the conduction of electricity. Others expected resistance to decrease linearly with temperature—a hypothesis that led to thermometer designs still in use today.
When the student cooled a mercury wire to 3.6 degrees above absolute zero, he found something remarkable: the electrical resistivity suddenly vanished.
Onnes quickly devised an ingenious experiment: as a diligent researcher, he knew that he needed to validate these surprising findings. He took a closed loop of wire, set a current running through it, and watched as it flowed endlessly without fading—a type of perpetual motion that seemed to defy everything we know about physics. And so, superconductivity was born.
More than a century later, all known superconductors still require extreme conditions like brutal cold or high pressure. If we could instead design a material that superconducts at room temperature, and under normal conditions, our world would be profoundly reshaped. “Room temperature superconductivity”, as it is generally called, would enable a raft of technological breakthroughs from affordable MRI machines to nearly lossless power grids.
Designing such a material means answering many open questions, and scientists are pursuing diverse strategies to find answers. One promising approach is light-induced superconductivity. In one astonishing study, researchers at the Max Planck Institute in Hamburg used light to entice a material that normally superconducts at roughly -180 °C to superconduct at room temperature - but only for a few picoseconds. This effect raised new questions: how does light achieve something that scientists have been grappling with for decades? What is the microscopic mechanism behind this phenomenon? Could understanding it unlock practical room-temperature superconductors?
Nature’s language is mathematics and mathematics is the language of the world’s most powerful quantum computer, Helios
Physics is a surprisingly profound field when you stop to think about it. At its core lies the idea that nature speaks the language of mathematics—and that by discovering the right equations, we can reveal her secrets. As bold as that sounds, history has proven it true time and again. Whenever we peek behind the veil; mathematics is there.
To understand a phenomena like superconductivity, physicists first need a mathematical model, or a set of equations that describe how it works. With the right model, they can predict and even design new superconductors that operate under more practical conditions. This is a key frontier in the search for room temperature superconductors, one of science’s holy grails.
Since the discovery of superconductivity, a lot of work has gone into finding this right model – one that can act as a sort of ‘Rosetta stone’ for harnessing this phenomenon. One of the best bets for describing high temperature superconductors like the one in the Hamburg study is called the “non-equilibrium Fermi-Hubbard” model, which describes how electrons interact and move in a crystal.
A surprising element of models that describe superconductivity is the prediction that electrons ‘pair up’ when the material becomes superconducting, dancing around in a waltz, two at a time. These pairs are referred to as “cooper pairs” after the famous physicist Leon Cooper. Now, scientists studying superconductors look for “pairing correlations”, a key signature of superconductivity.
Even armed with the Fermi-Hubbard model, light-induced superconductivity has been very difficult to study. The world’s most powerful supercomputers can only handle very small versions, limiting their utility. Even quantum platforms, like analog simulators, limit researchers to observing ‘average’ quantities and obscuring the microscopic details that are crucial for unravelling this mystery.
Light-induced superconductivity has proved challenging to study with quantum computers as well, as doing so requires low error rates, many qubits, and extreme flexibility to measure the fickle symptoms of superconductivity.
That was, until now: Quantinuum’s Helios is one of the first machines in the world able to handle the complexity of the non-equilibrium Fermi-Hibbard model at scales previously out of reach.
Hopping across the lattice and connecting the dots
Before Helios, we were limited to small explorations of this model, stalling research on this critical frontier. Now, with Helios, we have a quantum computer uniquely suited for this problem. With a novel fermionic encoding and using up to 90 qubits (72 system qubits plus 18 ancilla), Helios can simulate the dynamics of a 6×6 lattice — a system so large that its full quantum state spans over 2^72 dimensions.
Using Helios to study a system like this offers researchers a sort of “qubit-based laboratory.” Capable of handling complex quantum mechanical effects better than classical computers, Helios allows researchers to thoroughly explore phenomena like this without wasting expensive laboratory time and materials, or spending lots of money and energy running it on a supercomputer.
Our qubit-based laboratory is a dream come true for several reasons. First, it allows arbitrary state preparation – preparing states far from equilibrium, a challenging task for classical computers. Second, it allows for meaningfully long ‘dynamical simulation’ – seeing how the state evolves in time as entanglement spreads and complexity increases. This is notoriously difficult for classical computers, in part due to their difficulty with handling distinctly quantum phenomena like entanglement. Finally, it allows for flexible measurements and experimental parameters – you can measure any observable, including critical “off-diagonal” observables that carry the signature of superconductivity, and simulate any system, such as those with laser pulses or electric fields.
This last point is the most significant. While analog quantum simulators, like cold atom systems, can take snapshots of atom positions or measure densities, they struggle with off-diagonal observables—the very ones that signal the formation of Cooper pairs in superconductors.
Breaking new ground: a light-induced pairing
In our work, we've simulated three different regimes of the Fermi-Hubbard model and successfully measured non-zero superconducting pairing correlations — a first for any quantum computing platform.
We began by preparing a low-energy state of the model at half-filling — a standard benchmark for testing quantum simulations. Then, using simulated laser pulses or electric fields, we perturbed the system and observed how it responded.
After these perturbations, we measured a notable increase in the so-called “eta” pairing correlations, a mathematical signature of superconducting behavior. These results prove that our computers can help us understand light-induced superconductivity, such as the results from the Max Planck researchers. However, unlike those physical experiments, Helios offers a new level of control and insight. By tuning every aspect of the simulation — from pulse shape, to field strength, to lattice geometry — researchers can explore scenarios that are completely inaccessible to real materials or analog simulators.
Looking to a future where superconductors permeate our lives
Why does any of this matter? If we could predict which materials will become superconducting — and at what temperature, field, or current — it would transform how we search for new superconductors. Instead of trial-and-error in the lab, scientists could design and test new materials digitally first, saving huge amounts of time and money.
In the long run, Helios and its successors will become essential tools for materials science — not just confirming theories but generating new ones. And perhaps, one day, they’ll help us crack the code behind room-temperature superconductors.
Until then, the quantum revolution continues, one entangled pair at a time.
We’re pleased to introduce Helios, a technological marvel redefining the possible.
Building on its predecessor H2, which has already breached quantum advantage, Helios nearly doubles the qubit count and surpasses H2’s industry-leading fidelity, pushing further into the quantum advantage regime than any system before it. With unprecedented capability across its full stack, Helios is the most powerful quantum computer in the world.
“Helios is a true marvel—a seamless fusion of hardware and software, creating a platform for discovery unlike any other.”- Dr. Rajeeb Hazra, CEO
Helios’ groundbreaking design and advanced software stack bring quantum programming closer than ever to the ease and flexibility of classical computing—positioning Helios to accelerate commercial adoption. Even before its public debut, Helios had already demonstrated its capabilities as the world’s first enterprise-grade quantum computer. During a two-month early access program, select partners including SoftBank Corp. and JPMorgan Chase conducted commercially relevant research. We also leveraged Helios to perform large-scale simulations in high-temperature superconductivity and quantum magnetism—both with clear pathways to real-world industry applications.
Helios is now available to all customers through our cloud service and on-premise offering, including an option to integrate with NVIDIA GB200 for applications targeting specific end markets.
A Stellar Quantum Computer
“You would need to harvest every star in the universe to power a classical machine that could do the same calculations we did with Helios."
- Dr. Anthony Ransford, Helios Lead Architect
As we detailed in a benchmarking paper, Helios sets a new standard for quantum computing performance with the highest fidelity ever released to the market. It features 98 fully connected physical qubits with single-qubit gate fidelity of 99.9975% and two-qubit gate fidelity of 99.921% across all qubit pairs—making it the most accurate commercial quantum computer in the world.
Our fidelity shines in system-level benchmarks, such as Random Circuit Sampling (RCS), famously used by Google to demonstrate quantum supremacy when it performed an RCS task that would take a classical computer “10 septillion years” to replicate. Now, RCS serves as both a benchmark and the minimum standard for serious competitors in the market. Frequently missed in this conversation, however, is the importance of fidelity, or accuracy. That's why, when benchmarking Helios using RCS, we report the fidelity achieved by Helios on circuits of varying complexity (with complexity quantified by power requirements for classical simulation).
Our results show a classical supercomputer would require more power than the Sun—or, in fact, the combined power of all stars in the visible universe—to complete the same task in the same amount of time. In contrast, Helios achieved it using roughly the power of a single data center rack.
Like its predecessors, H1 and H2, Helios is designed to improve fidelity and overall system performance over time while sustaining competitive leadership through the launch of its successor.
Qubits at a Crossroads
"When I first saw the rotatable ion storage ring with a junction and gating legs sketched on a napkin, I loved the idea for its simplicity and efficiency. Seeing it finally realized after all of the team’s hard work has been truly incredible."
- Dr. John Gaebler, Fellow and Chief Scientist, Quantinuum
The Helios ion trap uses tiny currents to generate electromagnetic fields that hold single atomic ions (qubits) hovering above the trap for computation. We introduced a first-of-its-kind “junction”, which acts like a traffic intersection for qubits, enabling efficient routing and improved reliability. This is not only the first commercial implementation of this engineering triumph but it also allows our QCCD (Quantum Charged Coupled Device) architecture to scale, with future systems featuring hundreds of junctions arranged like a city street grid.
Whereas predecessor systems routed qubits using “physical swaps,” requiring sequential sorting, cooling, and gating that prevented parallel operations, the Helios QPU instead resembles a classical architecture with dedicated memory, cache, and computational zones. Like a spinning hard drive, the Helios QPU rotates qubits through ring storage (memory), passes them through the junction into the cache, moves them to logic zones for gating, and moves them to the leg storage while the next batch is processed. Sorting can now be done in parallel with cooling operations, resulting in a processor that is faster and less error prone. This parallelism will become a hallmark of Quantinuum’s future generations, enabling faster operating speeds.
Animation: This triumph of engineering demonstrates exquisite control over some of nature’s smallest particles in a way the world has never seen; one colleague likened the ions to a “little marching band.”
Quantinuum’s QCCD provides full all-to-all connectivity, giving the Helios QPU significant advantages over “fixed qubit” architectures, such as those used in superconducting systems. Its ability to physically move qubits around and entangle any qubit with any other qubit enables algorithms and error-correcting codes that are functionally impossible for fixed qubit architectures.
We made another “tiny” but significant change: we switched our qubits from ytterbium to barium. Whereas ytterbium largely relied on ultraviolet lasers that are expensive and hard on other components, barium can be manipulated with lasers in the visible part of the spectrum, where mature industrial technology exists, providing a more affordable, reliable and scalable commercial solution.
Barium also naturally allows the quantum computer to detect and remove a certain type of error, known as leakage, at the atomic level. By addressing this error directly, programmers can enhance the performance of their computation.
Delivered on Time – in Real Time
As announced earlier this year, Helios launched with a completely new stack equipped with a new software environment that makes quantum programming feel as intuitive as classical development.
Our new stack also features a real-time engine that massively improves our capability. With a real-time control system, we are evolving from static, pre-planned circuits to dynamic quantum programs that respond to results on the fly. We can now, for the first time on a quantum computer, interleave GPU-accelerated classical and quantum computations in a single program.
Our real-time engine also means we have dynamic transport – routing qubits as the moment demands reduces time to solution and diminishes the impact of memory errors.
Programmers can now use our new quantum programming language, Guppy, to write dynamic circuits that were previously impossible. By combining Guppy with our real-time engine, developers can leverage arbitrary control flow driven by quantum measurements, as well as full classical computation—including loops, higher-order functions, early exits, and dynamic qubit allocation. Far from being mere conveniences, these capabilities are essential stepping stones toward achieving fault-tolerant quantum computing at scale—putting us decisively ahead of the competition.
Fully compatible with industry standards like QIR and tools such as NVIDIA CUDA-Q, Helios bridges classical and quantum computing more seamlessly than ever, making hybrid quantum-classical development simple, natural, and accessible, and establishing Helios as the most programmable, general-purpose quantum computer ever built.
The Most Logical Path to Fault Tolerance
While everyone else is promising fault-tolerance, we’re delivering it. We are the only company to demonstrate a fully universal fault-tolerant gate set, we’ve demonstrated more codes than anyone else, and our logical fidelities are the best in class.
Now, with 98 physical qubits, we’ve been able to make 94 logical qubits, fully entangled in one of the largest GHZ states ever recorded. We did this with better than break-even fidelity, meaning they outperform physical qubits running the same algorithm. Built on our Iceberg code, published last year in Nature Physics, these logical qubits achieve the industry’s highest encoding efficiency, needing only two ancilla qubits per code block, or roughly a 1:1 physical-to-logical qubit ratio.
With 50 error-detected logical qubits, Helios achieved better than break-even performance, running the largest encoded simulation of quantum magnetism to date—an exceptional example of how users can leverage efficient encodings. This range and flexibility let users tailor the encoding rate to their application: fewer logical qubits deliver higher fidelity for less complex tasks, while larger sets enable more complex simulations.
Helios also produced 48 fully error-corrected logical qubits at a remarkable 2:1 encoding rate, a ratio thought impossible just a few years ago. This super high encoding rate stands in stark contrast to other notable demonstrations from industry peers. For example, the demonstration linked in the previous sentence would need a whopping 4800 qubits to make 48 logical qubits. Our 2:1 encoding rate was achieved through a clever technique called code concatenation, a breakthrough that supports single-shot error correction, transversal logic, and full parallelization—all at 99.99% state preparation and measurement fidelity.
To extend this performance at scale, all future Quantinuum systems—starting with Helios—will integrate real-time decoding using NVIDIA Grace Hopper GPUs, treating decoding as a dynamic computational process rather than a static lookup. Errors can be corrected as computations run without slowing the logical clock rate. Combined with Guppy, NVIDIA CUDA-Q, and NVQLink, this infrastructure forms the foundation for fault-tolerant, real-time quantum computation, delivering immediate quantum advantage in the near term and a clear path to scalable error-corrected computing.
We remain the only company to perform a fully universal fault-tolerant gate set, with more error-correcting codes and higher logical fidelities than any other company.
Helios is ready to drive practical, commercial quantum applications across industries. Its unprecedented fidelity, scalability, and programmability give users the tools to tackle problems that were previously out of reach. This is just the beginning, and we look forward to seeing what users and companies will achieve with it.