Abstract

Optimization problems are ubiquitous in all areas of science and engineering. Many important problems in chemistry, physics, and materials science can be cast as the optimization of a cost function under specific constraints. Traditionally, classical numerical optimization methods are the main go-to tool for studying such problems, but these methods are known to be grossly inefficient when tackling problems of this sort due to the exponentially increasing configuration space that needs to be explored. Quantum algorithms—protocols meant to be executed on quantum computers—provide a promising approach to solving such problems. Specifically, hybrid classical-quantum methods have been the focus of much attention in this context recently. The present project seeks to develop and experimentally implement a novel hybrid approach to solving optimization problems that reaches beyond the capabilities of the …

Date

2020

Authors

Amir Kalev, Itay Hen

Journal

NSF Award

Volume

20

Issue

2037301

Pages

37301