Quantum reservoir computing with repeated measurements on
  superconducting devices
Reservoir computing is a machine learning framework that uses artificial or
physical dissipative dynamics to predict time-series data using nonlinearity
and memory properties of dynamical systems. Quantum systems are considered as
promising reservoirs, but the conventional quantum reservoir computing (QRC)
models have problems in the execution time. In this paper, we develop a quantum
reservoir (QR) system that exploits repeated measurement to generate a
time-series, which can effectively reduce the execution time. We experimentally
implement the proposed QRC on the IBM's quantum superconducting device and show
that it achieves higher accuracy as well as shorter execution time than the
conventional QRC method. Furthermore, we study the temporal information
processing capacity to quantify the computational capability of the proposed
QRC; in particular, we use this quantity to identify the measurement strength
that best tradeoffs the amount of available information and the strength of
dissipation. 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.
Toshiki Yasuda Yudai Suzuki Tomoyuki Kubota Kohei Nakajima Qi Gao Wenlong Zhang Satoshi Shimono Hendra I. Nurdin Naoki Yamamoto