Temporal Information Processing on Noisy Quantum Computers
This work addresses the problem of enabling temporal processing applications on near-term quantum computers for fields like neural modeling and speech recognition, representing an incremental advance by adapting reservoir computing to quantum systems.
The authors tackled the challenge of temporal information processing on noisy quantum computers by proposing a universal quantum reservoir computing framework, and demonstrated through experiments on cloud-based superconducting quantum computers that small, noisy quantum reservoirs can handle high-order nonlinear temporal tasks.
The combination of machine learning and quantum computing has emerged as a promising approach for addressing previously untenable problems. Reservoir computing is an efficient learning paradigm that utilizes nonlinear dynamical systems for temporal information processing, i.e., processing of input sequences to produce output sequences. Here we propose quantum reservoir computing that harnesses complex dissipative quantum dynamics. Our class of quantum reservoirs is universal, in that any nonlinear fading memory map can be approximated arbitrarily closely and uniformly over all inputs by a quantum reservoir from this class. We describe a subclass of the universal class that is readily implementable using quantum gates native to current noisy gate-model quantum computers. Proof-of-principle experiments on remotely accessed cloud-based superconducting quantum computers demonstrate that small and noisy quantum reservoirs can tackle high-order nonlinear temporal tasks. Our theoretical and experimental results pave the path 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 including neural modeling, speech recognition and natural language processing, going beyond static classification and regression tasks.