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.
@article{arxiv.2106.14594,
title = {Adaptive Random Quantum Eigensolver},
author = {Nancy Barraza and Chi-Yue Pan and Lucas Lamata and Enrique Solano and Francisco Albarrán-Arriagada},
journal= {arXiv preprint arXiv:2106.14594},
year = {2022}
}