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

Carlos Hernani-Morales, Gabriel Alvarado, Francisco Albarrán-Arriagada, Yolanda Vives-Gilabert, Enrique Solano +1 more
9/10/2023
quant-phcs.AIcs.LG

No official code found. Best implementation: carlos-hernani/QuantumMemristor (4 stars, MIT, last commit 2023-10-16).

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.

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Code Implementations(1)

carlos-hernani/QuantumMemristorCommunity100%
MIT

Official Repository for "Maximization of Memristive Behaviour Using Machine Learning"

40Python, Jupyter NotebookJun 5, 20233 years agoMIT

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Cite this paper

@article{hernanimorales2023machine,
  title  = {Machine Learning for maximizing the memristivity of single and coupled quantum memristors},
  author = {Carlos Hernani-Morales and Gabriel Alvarado and Francisco Albarrán-Arriagada and Yolanda Vives-Gilabert and Enrique Solano and José D. Martín-Guerrero},
  year   = {2023},
  eprint = {2309.05062},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2309.05062v1}
}

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