  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.
