Harnessing disordered quantum dynamics for machine learning

TL;DR

This paper introduces quantum reservoir computing, leveraging natural quantum dynamics to perform machine learning tasks, potentially overcoming current quantum computer implementation challenges.

Contribution

It proposes a novel quantum reservoir computing platform that emulates nonlinear dynamics, including chaos, using small quantum systems for AI applications.

Findings

Quantum systems with up to seven qubits match classical neural network performance.

Demonstrates feasibility of quantum reservoir computing for machine learning.

Opens new paradigm for AI using quantum physics.

Abstract

Quantum computer has an amazing potential of fast information processing. However, realisation of a digital quantum computer is still a challenging problem requiring highly accurate controls and key application strategies. Here we propose a novel platform, quantum reservoir computing, to solve these issues successfully by exploiting natural quantum dynamics, which is ubiquitous in laboratories nowadays, for machine learning. In this framework, nonlinear dynamics including classical chaos can be universally emulated in quantum systems. A number of numerical experiments show that quantum systems consisting of at most seven qubits possess computational capabilities comparable to conventional recurrent neural networks of 500 nodes. This discovery opens up a new paradigm for information processing with artificial intelligence powered by quantum physics.