Optimized Quantum Algorithms: Breakthrough Toward Quantum Advantage

Optimized quantum algorithms present solution to Fermi-Hubbard model on near-term hardware.

Researchers from the University of Bristol and quantum start-up, Phasecraft, have advanced quantum computing research, bringing practical hybrid quantum-classical computing one step closer.

The team, led by Bristol researcher and Phasecraft co-founder, Dr. Ashley Montanaro, has discovered algorithms and analysis which significantly lessen the quantum hardware capability needed to solve problems that go beyond the realm of classical computing, even supercomputers.

In the paper, published in Physical Review B, the team demonstrates how optimized quantum algorithms can solve instances of the notorious Fermi-Hubbard model on near-term hardware.

The Fermi-Hubbard model is of fundamental importance in condensed-matter physics as a model for strongly correlated materials and a route to understanding high-temperature superconductivity.

Finding the ground state of the Fermi-Hubbard model has been predicted to be one of the first applications of near-term quantum computers and one that offers a pathway to understanding and developing novel materials.

Dr. Ashley Montanaro, research lead and co-founder of Phasecraft: “Quantum computing has critically important applications in materials science and other domains. Despite the major quantum hardware advances recently, we may still be several years from having the right software and hardware to solve meaningful problems with quantum computing. Our research focuses on algorithms and software optimizations to maximize the quantum hardware’s capacity, and bring quantum computing closer to reality.

“Near-term quantum hardware will have limited device and computation size. Phasecraft applied new theoretical ideas and numerical experiments to put together a very comprehensive study on different strategies for solving the Fermi-Hubbard model, zeroing in on strategies that are most likely to have the best results and impact in the near future.”

Lana Mineh, a PhD student in the School of Mathematics and the Centre for Doctoral Training in Quantum Engineering, who played a key role in the research, said, “The results suggest that optimizing over quantum circuits with a gate depth substantially less than a thousand could be sufficient to solve instances of the Fermi-Hubbard model beyond the capacity of current supercomputers. This new research shows significant promise for producing the ground state of the model on near-term quantum devices, improving on previous research findings by around a factor of 10.”

Physical Review B, published by the American Physical Society, is the top specialist journal in condensed-matter physics. The peer-reviewed research paper was also chosen as the Editors’ Suggestion and to appear in Physics magazine.

Andrew Childs, Professor in the Department of Computer Science and Institute for Advanced Computer Studies at the University of Maryland: “The Fermi-Hubbard model is a major challenge in condensed-matter physics, and the Phasecraft team has made impressive steps in showing how quantum computers could solve it. Their work suggests that surprisingly low-depth circuits could provide useful information about this model, making it more accessible to realistic quantum hardware.”

Hartmut Neven, Head of Quantum Artificial Intelligence Lab, Google: “Sooner or later, quantum computing is coming. Developing the algorithms and technology to power the first commercial applications of early quantum computing hardware is the toughest challenge facing the field, which few are willing to take on. We are proud to be partners with Phasecraft, a team that are developing advances in quantum software that could shorten that timeframe by years.”

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