Temporal Information Processing on Noisy Quantum Computers

Abstract

The combination of machine learning and quantum computing has emerged as a promising approach for addressing previously untenable problems. Reservoir computing is a state-of-the-art machine learning paradigm that utilizes nonlinear dynamical systems for temporal information processing, whose state-space dimension plays a key role in the performance. Here we propose a quantum reservoir system that harnesses complex dissipative quantum dynamics and the exponentially large quantum state-space. Our proposal is readily implementable on available noisy gate-model quantum processors and possesses universal computational power for approximating nonlinear short-term memory maps, important in applications such as neural modeling, speech recognition and natural language processing. We experimentally demonstrate on superconducting quantum computers that small and noisy quantum reservoirs can tackle high-order nonlinear temporal tasks. Our theoretical and experimental results pave the way for attractive temporal processing applications of near-term gate-model quantum computers of increasing fidelity but without quantum error correction, signifying the potential of these devices for wider applications beyond static classification and regression tasks in interdisciplinary areas.