Neuromorphic computing with single-element quantum reservoirs

We study the noise-resilient neuromorphic computing scheme of reservoir computing with a quantum system as a reservoir. We consider quantum reservoirs formed by a single physical element, such as can be implemented in near-term, NISQ-era devices by a quantum nonlinear oscillator. By studying the performance of our single-element reservoirs on signal processing and memory capacity benchmarks, we demonstrate computational capability expanding with Hilbert space dimension, and quantum advantage arising from the intrinsic nonlinearity of quantum measurement. Beyond quantum reservoir computing, the latter may have impact across quantum machine learning. We study the impact of realistic experimental conditions such as noise and parameter fluctuations, and discuss near-term implementations. Our results show that single-element quantum reservoir computing is an attractive modality for quantum information processing on near-term hardware.