Quantinuum H-Series quantum computer accelerates through 3 more performance records for quantum volume
217, 218, and 219
In the last 6 months, Quantinuum H-Series hardware has demonstrated explosive performance improvement. Quantinuum’s System Model H1-1, Powered by Honeywell, has demonstrated going from 214 = 16,384 quantum volume (QV) announced in February 2023 to now 219 = 524,288, with all the details and data released on our GitHub repository for full transparency. At a quantum volume of 524,288, H1-1 is 1000x higher than the next best reported quantum volume.
We set a big goal back in 2020 when we launched our first quantum computer, HØ. HØ was launched with six qubits and a quantum volume of 26 = 64, and at that time we made the bold and audacious commitment to increasing the quantum volume of our commercial machines 10x per year for 5 years, equating to a quantum volume of 8,388,608 or 223 by the end of 2025. In an industry that is often accused of being over-hyped, a commitment like this was easy to forget. But we did not forget. Diligently, our scientists and engineers continued to achieve world-record after world-record in a tireless and determined pursuit to systematically improve the overall performance of our quantum computers. As seen in Figure 1, from 2020 to early 2023, we have steadily been increasing the quantum volume to demonstrate that increased qubit count while reducing errors directly translates to more computational power. Just within 2023 we’ve had multiple announcements of quantum volume improvements. In February we announced that H1-1 had leapfrogged 214 and achieved a quantum volume of 215. In May 2023, we launched H2-1 with 32 qubits at a quantum volume of 216. Now we are thrilled to announce the sequential improvements of 217, 218, and 219, all on H1-1.
Importantly, none of these results were “hero results”, meaning there are no special calibrations made just to try to make the system look better. Our quantum volume data is taken on our commercial systems interwoven with customer jobs. What we experience is what our customers experience. Instead of improving at 10x per year as we committed back in 2020, the pace of improvement over the past 6 months has been 30x, accelerating at least one year from our 5-year commitment. While these demonstrations were made using H1-1, the similarities in the designs of H1-2 (now upgraded with 20 qubits) and H2-1, our recently released second generation system, make it straightforward to share the improvements from one machine to another and achieve the same results.
In this young and rapidly evolving industry, there are and will be disagreements about which benchmarks are best to use. Quantum volume, developed by IBM, is undeniably rigorous. Quantum volume can be measured on any gate-based machine. Quantum volume has been peer-reviewed and has well defined assumptions and processes for making the measurements. Improvements in QV require consistent reductions in errors, making it likely that no matter the application, QV improvements translate to better performance. In fact, to realize the exponential increase in power that quantum computers promise, it is required to continue to reduce these error rates. The average two-qubit gate error with these three new QV demonstrations was 0.13%, the best in the industry. We measure many benchmarks, but it is for these reasons that we have adopted quantum volume as our primary system-wide benchmark to report our performance.
Putting aside the argument of which benchmark is better, year-over-year improvements in a rigorous benchmark do not happen accidentally. It can only happen because the dedicated, talented scientists and engineers that work on H-Series hardware have a deep understanding of its error model and a deep understanding of how to reduce the errors to make overall performance improvements. Equally important the talented scientists and engineers have mastery of their domain expertise and can dream-up and then implement the improvements. These validated error models become the bedrock of future systems’ design, instilling confidence that those systems will have well understood error models, and the performance of those systems can also be systematically improved and ultimate performance goals achieved. Taking nothing away from those talented scientists and engineers, but having perfect, identical qubits and employing our quantum charge coupled device (QCCD) architecture does give us an advantage that all the other architectures and other modalities do not have.
What should potential users of H-Series quantum computers take away from this write-up (and what do current users already know)?
- Quantinuum is committed to systematically improving the core performance of our quantum computing hardware. The better the fundamental performance, the lower the overhead will be when doing error mitigation, error detection, and ultimately error correction. This provides confidence in our ability to deliver fault-tolerant compute capabilities.
- Progress on your research, use-case, or application can be accelerated by getting access to H-series technology because our quantum computers can do circuits that other technologies cannot. “It actually works!” exclaim excited first-time users.
- Quantinuum intends to continue to be the quantum computing company that quietly over-delivers, even on big goals.
1. https://github.com/CQCL/quantinuum-hardware-quantum-volume
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.
For a novel technology to be successful, it must prove that it is both useful and works as described.
Checking that our computers “work as described” is called benchmarking and verification by the experts. We are proud to be leaders in this field, with the most benchmarked quantum processors in the world. We also work with National Laboratories in various countries to develop new benchmarking techniques and standards. Additionally, we have our own team of experts leading the field in benchmarking and verification.
Currently, a lot of verification (i.e. checking that you got the right answer) is done by classical computers – most quantum processors can still be simulated by a classical computer. As we move towards quantum processors that are hard (or impossible) to simulate, this introduces a problem: how can we keep checking that our technology is working correctly without simulating it?
We recently partnered with the UK’s Quantum Software Lab to develop a novel and scalable verification and benchmarking protocol that will help us as we make the transition to quantum processors that cannot be simulated.
This new protocol does not require classical simulation, or the transfer of a qubit between two parties. The team’s “on-chip” verification protocol eliminates the need for a physically separated verifier and makes no assumptions about the processor’s noise. To top it all off, this new protocol is qubit-efficient.
The team’s protocol is application-agnostic, benefiting all users. Further, the protocol is optimized to our QCCD hardware, meaning that we have a path towards verified quantum advantage – as we compute more things that cannot be classically simulated, we will be able to check that what we are doing is right.
Running the protocol on Quantinuum System Model H1, the team ended up performing the largest verified Measurement Based Quantum Computing (MBQC) circuit to date. This was enabled by our System Model H1’s low cross-talk gate zones, mid-circuit measurement and reset, and long coherence times. By performing the largest verified MBQC computation to date, and by verifying computations significantly larger than any others to be verified before, we reaffirm the Quantinuum Systems as best-in-class.
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.
Find the Quantinuum team at this year’s SC24 conference from November 17th – 22nd in Atlanta, Georgia. Meet our team at Booth #4351 to discover how Quantinuum is bridging the gap between quantum computing and high-performance compute with leading industry partners.
The Quantinuum team will be participating various events, panels and poster sessions to showcase our quantum computing technologies. Join us at the below sessions:
Monday, Nov 18, 8:00 - 8:25pm, EST
Panel: KAUST booth 1031
Nash Palaniswamy, Quantinuum’s CCO, will join a panel discussion with quantum vendors and KAUST partners to discuss advancements in quantum technology.
Monday, Nov 18, 9:00 - 11:59pm, EST
Beowulf Bash: World of Coca-Cola
This year, we are proudly sponsoring the Beowulf Bash, a unique event organized to bring the HPC community together for a night of unique entertainment! Join us at the event on Monday, November 18th, 9:00pm at the World of Coca-Cola.
Wednesday, Nov 20, 3:30 – 5:00pm, EST
Panel: Educating for a Hybrid Future: Bridging the Gap between High-Performance and Quantum Computing
Vincent Anandraj, Quantinuum’s Director of Global Ecosystem and Strategic Alliances, will moderate this panel which brings together experts from leading supercomputing centers and the quantum computing industry, including PSC, Leibniz Supercomputing Centre, IQM Quantum Computers, NVIDIA, and National Research Foundation.
Thursday, Nov 21, 11:00 – 11:30am, EST
Presentation: Realizing Quantum Kernel Models at Scale with Matrix Product State Simulation
Pablo Andres-Martinez, Research Scientist at Quantinuum, will present research done in collaboration with HSBC, where the team applied quantum methods to fraud detection.