Taking advantage of noise in quantum reservoir computing

Abstract

The biggest challenge that quantum computing and quantum machine learning are currently facingis the presence of noise in quantum devices. As a result, big efforts have been put into correctingor mitigating the induced errors. But, can these two fields benefit from noise? Surprisingly, wedemonstrate that under some circumstances, quantum noise can be used to improve the performanceof 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, whilethe depolarizing and phase damping noises should be prioritized for correction. This critical resultsheds new light into the physical mechanisms underlying quantum devices, providing solid practicalprescriptions for a successful implementation of quantum information processing in nowadayshardware.