Adaptive Random Quantum Eigensolver

• URL: http://arxiv.org/abs/2106.14594v2
• Date: Thu, 5 May 2022 20:27:17 GMT
• Title: Adaptive Random Quantum Eigensolver
• Authors: Nancy Barraza, Chi-Yue Pan, Lucas Lamata, Enrique Solano, Francisco Albarr\'an-Arriagada
• Abstract summary: We introduce a general method to parametrize and optimize the probability density function of a random number generator. Our optimization is based on two figures of merit: learning speed and learning accuracy.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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