Adaptive random quantum eigensolver

Description

We propose an adaptive random quantum algorithm to obtain an optimized eigensolver. Specifically, we introduce a general method to parametrize and optimize the probability density function of a random number generator, which is the core of stochastic algorithms. We follow a bioinspired evolutionary mutation method to introduce changes in the involved matrices. Our optimization is based on two figures of merit: learning speed and learning accuracy. This method provides high fidelities for the searched eigenvectors and faster convergence on the way to quantum advantage with current noisy intermediate-scaled quantum computers.

Abstract

Junta de Andalucía (Grants No. P20-00617 and No. US-1380840)

Abstract

Science and Technology Commission of Shanghai Municipality (Grant No. 2019SHZDZX01-ZX04)

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