Machine Learning for Maximizing the Memristivity of Single and Coupled Quantum Memristors-Reference-Cited by-同舟云学术

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

Published:2024-04-14 Issue: Volume: Page:
ISSN:2511-9044
Container-title:Advanced Quantum Technologies
language:en
Short-container-title:Adv Quantum Tech

Author:

Hernani‐Morales Carlos¹²^ORCID,Alvarado Gabriel³,Albarrán‐Arriagada Francisco⁴⁵,Vives‐Gilabert Yolanda²,Solano Enrique³,Martín‐Guerrero José D.²⁶

Affiliation:

1. Quantum Spain Universitat de València C/Dr. Moliner, 50, Burjassot Valencia 46100 Spain

2. IDAL, Electronic Engineering Department ETSE‐UV, Universitat de València Avgda, Universitat s/n, Burjassot Valencia 46100 Spain

3. Kipu Quantum Greifswalderstrasse 226 10405 Berlin Germany

4. Departamento de Física Universidad de Santiago de Chile (USACH) Avenida Víctor Jara 3493 Santiago 9170124 Chile

5. Center for the Development of Nanoscience and Nanotechnology Estación Central 9170124 Chile

6. Valencian Graduate School and Research Network of Artificial Intelligence (ValgrAI) Valencia 46022 Spain

Abstract

AbstractMachine 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.

Funder

Generalitat Valenciana

Agencia Nacional de Investigación y Desarrollo

Publisher

Wiley

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/qute.202300294

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