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

09/10/2023
by   Carlos Hernani-Morales, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/22/2020

Classification with Quantum Machine Learning: A Survey

Due to the superiority and noteworthy progress of Quantum Computing (QC)...
research
10/23/2017

Squeezed state evolution and entanglement in lossy coupled resonator optical waveguides

We investigate theoretically the temporal evolution of a squeezed state ...
research
06/17/2021

Impurity-induced increase in the thermal quantum correlations and teleportation in an Ising-XXZ diamond chain

In this work we analyze the quantum correlations in a spin-1/2 Ising-XXZ...
research
07/27/2018

Quantized Hodgkin-Huxley Model for Quantum Neurons

The Hodgkin-Huxley model describes the behavior of the membrane voltage ...
research
06/26/2023

Deep Bayesian Experimental Design for Quantum Many-Body Systems

Bayesian experimental design is a technique that allows to efficiently s...
research
09/04/2023

Soft-Dropout: A Practical Approach for Mitigating Overfitting in Quantum Convolutional Neural Networks

Quantum convolutional neural network (QCNN), an early application for qu...
research
06/06/2023

Transition role of entangled data in quantum machine learning

Entanglement serves as the resource to empower quantum computing. Recent...

Please sign up or login with your details

Forgot password? Click here to reset