Artículo
Adaptive random quantum eigensolver
Autor/es | Barraza, N.
Pan, C.-Y. Lamata Manuel, Lucas Solano, E. Albarrán Arriagada, Francisco |
Departamento | Universidad de Sevilla. Departamento de Física Atómica, Molecular y Nuclear |
Fecha de publicación | 2022 |
Fecha de depósito | 2022-05-09 |
Publicado en |
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Resumen | 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, ... 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. |
Agencias financiadoras | Junta de Andalucía Science and Technology Commission of Shanghai Municipality |
Identificador del proyecto | P20-00617
US-1380840 2019SHZDZX01-ZX04 |
Cita | Barraza, N., Pan, C.-., Lamata Manuel, L., Solano, E. y Albarrán Arriagada, F. (2022). Adaptive random quantum eigensolver. Physical Review A, 105 (5), 052406. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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PhysRevA.105.052406.pdf | 948.7Kb | [PDF] | Ver/ | |