arXiv:2106.14594 · Physical Review A

Adaptive random quantum eigensolver

N. Barraza, C.-Y. Pan, Lucas Lamata, E. Solano, F. Albarrán-Arriagada

Published May 4, 2022• 3 citations

Abstract

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.

DOI: 10.1103/physreva.105.052406

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