Quantum memristors for neuromorphic quantum machine learning

TL;DR

This paper explores quantum memristors as a novel hardware component that combines unitary evolution with nonlinearity, potentially enabling neuromorphic quantum machine learning on near-term quantum devices.

Contribution

It introduces the concept of quantum memristors as a promising approach for implementing neuromorphic quantum computing in quantum machine learning.

Findings

Quantum memristors enable nonlinear dynamics in quantum systems.

Potential for more efficient quantum machine learning calculations.

Facilitates neuromorphic quantum computing architectures.

Abstract

Quantum machine learning may permit to realize more efficient machine learning calculations with near-term quantum devices. Among the diverse quantum machine learning paradigms which are currently being considered, quantum memristors are promising as a way of combining, in the same quantum hardware, a unitary evolution with the nonlinearity provided by the measurement and feedforward. Thus, an efficient way of deploying neuromorphic quantum computing for quantum machine learning may be enabled.