Taking advantage of noise in quantum reservoir computing

TL;DR

This paper reveals that certain types of noise in quantum devices can actually enhance quantum reservoir computing performance, challenging the conventional view that noise is solely detrimental.

Contribution

It demonstrates that amplitude damping noise can improve quantum reservoir computing, offering new insights into noise management in quantum machine learning.

Findings

Amplitude damping noise can be beneficial for quantum machine learning.

Depolarizing and phase damping noises should be corrected.

Noise can be exploited to improve quantum reservoir computing performance.

Abstract

The biggest challenge that quantum computing and quantum machine learning are currently facing is the presence of noise in quantum devices. As a result, big efforts have been put into correcting or mitigating the induced errors. But, can these two fields benefit from noise? Surprisingly, we demonstrate that under some circumstances, quantum noise can be used to improve the performance of quantum reservoir computing, a prominent and recent quantum machine learning algorithm. Our results show that the amplitude damping noise can be beneficial to machine learning, while the depolarizing and phase damping noises should be prioritized for correction. This critical result sheds new light into the physical mechanisms underlying quantum devices, providing solid practical prescriptions for a successful implementation of quantum information processing in nowadays hardware.