通过机器学习最大化单个和耦合量子晶体记忆器的记忆性

Advanced Quantum Technologies Pub Date : 2024-04-15 DOI:10.1002/qute.202300294

Carlos Hernani‐Morales, Gabriel Alvarado, Francisco Albarrán‐Arriagada, Yolanda Vives‐Gilabert, Enrique Solano, José D. Martín‐Guerrero

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摘要

本文提出了机器学习（ML）方法来描述单个和耦合量子忆阻器的忆阻特性。结果表明，忆阻性最大化会导致两个量子忆阻器的纠缠程度达到较大值，从而揭示了量子相关性与记忆之间的密切关系。研究结果加强了将量子忆阻器用作神经形态量子计算关键组件的可能性。

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

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.

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Advanced Quantum Technologies

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