Adaptive Random Quantum Eigensolver
arXiv:2106.14594 · doi:10.1103/PhysRevA.105.052406
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.
arXiv comment7+5 pages, 9 figures, 2 tables
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