Adaptive random quantum eigensolver
Nicolás Barraza, C. Y. Pan, Lucas Lamata, E. Solano, F. Albarrán-Arriagada · Physical Review A · 2022
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.