Machine Learning for Maximizing the Memristivity of Single and Coupled Quantum Memristors

Hernani-Morales, Carlos; Alvarado, Gabriel; Albarran-Arriagada, Francisco; Vives-Gilabert, Yolanda; Solano, Enrique; Martin-Guerrero, Jose D.

Abstract

--- - Machine learning (ML) methods are proposed to characterize the memristive properties of single and coupled quantum memristors. It is shown that maximizing the memristivity leads to large values in the degree of entanglement of two quantum memristors, unveiling the close relationship between quantum correlations and memory. The results strengthen the possibility of using quantum memristors as key components of neuromorphic quantum computing. - Machine learning (ML) methods are proposed to characterize the memristive properties of single and coupled quantum memristors. It is shown that maximizing the memristivity leads to large values in the degree of entanglement of two quantum memristors, unveiling the close relationship between quantum correlations and memory. The results strengthen the possibility of using quantum memristors as key components of neuromorphic quantum computing. image

Más información

Título según WOS: Machine Learning for Maximizing the Memristivity of Single and Coupled Quantum Memristors
Título de la Revista: ADVANCED QUANTUM TECHNOLOGIES
Editorial: Wiley
Fecha de publicación: 2024
DOI:

10.1002/qute.202300294

Notas: ISI