Adaptive Random Quantum Eigensolver

TL;DR

This paper introduces an adaptive random quantum eigensolver that optimizes the probability distribution of stochastic algorithms to achieve high fidelity eigenvectors and faster convergence on noisy quantum computers.

Contribution

It presents a novel adaptive method to optimize the probability density function in stochastic quantum eigensolvers using bioinspired mutation techniques.

Findings

Achieves high fidelities for eigenvectors.

Faster convergence towards quantum advantage.

Effective on noisy intermediate-scale quantum computers.

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.