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Adaptive random quantum eigensolver
N. Barraza, C. Pan, L. Lamata, E. Solano, F. Albarr'an-Arriagada·June 28, 2021·DOI: 10.1103/PhysRevA.105.052406
Physics
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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 bio-inspired 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 (NISQ) computers.