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Reservoir computing is a machine learning framework that uses artificial orphysical dissipative dynamics to predict time-series data using nonlinearityand memory properties of dynamical systems. Quantum systems are considered aspromising reservoirs, but the conventional quantum reservoir computing (QRC)models have problems in the execution time. In this paper, we develop a quantumreservoir (QR) system that exploits repeated measurement to generate atime-series, which can effectively reduce the execution time. We experimentallyimplement the proposed QRC on the IBM's quantum superconducting device and showthat it achieves higher accuracy as well as shorter execution time than theconventional QRC method. Furthermore, we study the temporal informationprocessing capacity to quantify the computational capability of the proposedQRC; in particular, we use this quantity to identify the measurement strengththat best tradeoffs the amount of available information and the strength ofdissipation. An experimental demonstration with soft robot is also provided,where the repeated measurement over 1000 timesteps was effectively applied.Finally, a preliminary result with 120 qubits device is discussed.

Quantum reservoir computing with repeated measurements on superconducting devices