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

Hernani-Morales, Carlos; Alvarado, Gabriel; 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. © 2024 The Authors. Advanced Quantum Technologies published by Wiley-VCH GmbH.

Más información

Título según WOS: Machine Learning for Maximizing the Memristivity of Single and Coupled Quantum Memristors
Título según SCOPUS: Machine Learning for Maximizing the Memristivity of Single and Coupled Quantum Memristors
Título de la Revista: Advanced Quantum Technologies
Editorial: John Wiley and Sons Inc.
Fecha de publicación: 2024
Idioma: English
DOI:

10.1002/qute.202300294

Notas: ISI, SCOPUS