Algorithms reduce quantum hardware requirement

Author: EIS Release Date: Jan 11, 2021

Researchers from the University of Bristol and quantum start-up, Phasecraft, have discovered algorithms and analysis which significantly lessen the quantum hardware capability needed to solve problems which go beyond the realm of supercomputers.

The research demonstrates how optimised quantum algorithms can solve the 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.

”Our research focuses on algorithms and software optimisations to maximise the quantum hardware’s capacity, and bring quantum computing closer to reality,” says Phasecraft co-founder Ashley Montanaro.
“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.
Phasecraft has closed a seed round funding with £3.7m from private-sector VC investors, led by LocalGlobe with Episode1 along with previous investors.
Phasecraft previously raised a £750,000 pre-seed round led by UCL Technology Fund with Parkwalk Advisors and London Co-investment Fund and has earned several grants facilitated by InnovateUK.
Between equity funding and research grants, Phasecraft has raised more than £5.5m.