Collaborators observe quantum speed-up in optimization problems

A breakthrough application of neutral-atom quantum processors has been demonstrated by a collaboration between Harvard University and scientists at QuEra Computing.

The study was co-led by the George Vasmer Leverett Professor of Physics at Harvard and co-director of the Harvard Quantum Initiative, as well as by the Lester Wolfe Professor of Physics at MIT. The research was published in Science on May 5, 2022.

The neutral-atom quantum processor was previously proposed to efficiently solve hard combinatorial problems. In this landmark publication, the authors deploy the first implementation of efficient quantum optimization on a real quantum computer, as well as showcase unprecedented quantum hardware power.

The calculations were performed on Harvard's quantum processor with effective circuit depths up to 32. The large system size and circuit depth made it impossible to use classical simulations to pre-optimize the control parameters. A closed loop deployment of a quantum-classical hybrid was required.

The combination of system size, circuit depth, and outstanding quantum control culminated in a quantum leap. The team identified cases that challenged classical computers, but that were more efficiently solved with the neutral-atom quantum processor. A super-linear quantum speed-up was found. GenericTensorNetworks.jl and Bloqade.jl were instrumental in discovering hard instances and understanding quantum performance.

A deep understanding of the underlying physics of the quantum algorithm as well as the fundamental limitations of its classical counterpart allowed us to realize ways for the quantum machine to achieve a speedup.

In the near future, to extract as much quantum power as possible, it is critical to identify problems that can be mapped to the specific quantum architecture, with little to no overhead.

Themaximum independent set problem, solved by the team, is a hard task in computer science and can be used in many areas. The path for applying quantum computing to cater to real-world industrial and social needs is paved by the identification of classically challenging problem instances.

The results represent the first step in bringing quantum advantage to hard optimization problems. The presence of a quantum speedup for hard problem instances is very encouraging. The results help us develop better hardware to tackle some of the most relevant computational problems.

More information: S. Ebadi et al, Quantum optimization of maximum independent set using Rydberg atom arrays, Science (2022). DOI: 10.1126/science.abo6587 Journal information: Science Citation: Collaborators observe quantum speed-up in optimization problems (2022, May 5) retrieved 5 May 2022 from https://phys.org/news/2022-05-collaborators-quantum-speed-up-optimization-problems.html This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.