Quantinuum H-Series Quantum Computer Registers Three More Quantum Volume Performance Records

Quantinuum quantum volume

In the last six months, Quantinuum’s H-Series hardware has demonstrated an extraordinary leap in performance, recording three massive jumps in quantum volume, according to a recent blog post by the team.

According to the post, their flagship system, the System Model H1-1 Powered by Honeywell, has witnessed a progression from a quantum volume (QV) of 16,384 in February 2023 to the latest mark of 524,288. The team has shared all the details and data on their GitHub repository, emphasizing their commitment to transparency.

“At a quantum volume of 524,288, H1-1 surpasses the next best reported quantum volume by 1000 times,” states the blog post, underscoring their achievement.

The accomplishment aligns with their ambitious goal set in 2020 with the launch of their first quantum computer, HØ. Back then, HØ possessed six qubits and a quantum volume of 64. They boldly committed to increasing the quantum volume of their commercial machines by 10 times per year for five years, aiming for a quantum volume of 8,388,608 (or 223) by the end of 2025.

Quantinuum has already issued multiple announcements of quantum volume advancements in the first half of 2023. In February, they unveiled that H1-1 had surpassed 214, achieving a quantum volume of 215. Then, in May 2023, they introduced H2-1, a second-generation system equipped with 32 qubits and a quantum volume of 216. Now, the team is thrilled to disclose the sequential improvements of 217, 218, and 219, all achieved on H1-1.

Crucially, the reported results are not artificially enhanced but rather obtained from commercial systems working on real customer jobs. The team emphasizes that their customers experience the same performance as reflected in the quantum volume data.

The pace of improvement has already accelerated beyond their five-year projection.

The demonstration also reveals the versatility of Quantinuum’s system: “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.”

The choice of quantum volume as their primary benchmark is rooted in its rigorous nature and broad applicability, according to the post. Developed by IBM, quantum volume can be measured on any gate-based machine, following well-defined assumptions and processes. Reducing errors consistently is crucial for advancing quantum volume, as it directly translates to improved performance across various applications. Notably, the team achieved an industry-leading average two-qubit gate error of 0.13% with their latest quantum volume demonstrations.

The team acknowledges the ongoing debate around benchmark preferences in the industry but underscores the significance of year-over-year improvements in a rigorous benchmark. These advancements are not accidental but the result of the diligent work by their dedicated scientists and engineers, who possess a profound understanding of error models and the expertise to drive overall performance enhancements.

“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,” the team reports.

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Matt Swayne

With a several-decades long background in journalism and communications, Matt Swayne has worked as a science communicator for an R1 university for more than 12 years, specializing in translating high tech and deep tech for the general audience. He has served as a writer, editor and analyst at The Quantum Insider since its inception. In addition to his service as a science communicator, Matt also develops courses to improve the media and communications skills of scientists and has taught courses. [email protected]

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The Future of Materials Discovery: Reducing R&D Costs significantly with GenMat’s AI and Machine Learning Tools

When: July 13, 2023 at 11:30am

What: GenMat Webinar

Jake Vikoren

Jake Vikoren

Company Speaker

Deep Prasad

Deep Prasad

Company Speaker

Araceli Venegas

Araceli Venegas

Company Speaker

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