Machine Learning for maximizing the memristivity of single and coupled quantum memristors

TL;DR

This paper demonstrates how machine learning can optimize the memristive properties of quantum memristors, revealing a link between high memristivity and quantum entanglement, with implications for neuromorphic quantum computing.

Contribution

It introduces ML methods to enhance quantum memristor memristivity and uncovers the relationship between memristivity and quantum entanglement.

Findings

Maximizing memristivity increases entanglement between quantum memristors.

Quantum correlations are closely linked to memristive behavior.

Results support using quantum memristors in neuromorphic quantum computing.

Abstract

We propose machine learning (ML) methods to characterize the memristive properties of single and coupled quantum memristors. We show 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. Our results strengthen the possibility of using quantum memristors as key components of neuromorphic quantum computing.