Enforcing Fading Memory of Noisy Quantum Echo State Networks

Reservoir computing is a versatile paradigm in computational neuroscience and machine learning that uses recurrent neural networks to process time-dependent inputs. We leverage noise in gate-based quantum computers to enforce the fading memory property of a quantum circuit acting as a reservoir. We test the learning architecture on standard bench-marking tasks, such as NARMA and short-term memory tasks, by providing numerical evidence supporting the existence of an optimal noise regime that enhances predictive performances. This critical behavior indicates the presence of a regime in which several network capabilities, such as short-term memory capacity and expressivity, are maximized. Our results pave the way for the efficient use of current noisy quantum devices in quantum machine learning applications.