  Reservoir computing is a temporal information processing system that exploits
artificial or physical dissipative dynamics to learn a dynamical system and
generate the target time-series. This paper proposes the use of real
superconducting quantum computing devices as the reservoir, where the
dissipative property is served by the natural noise added to the quantum bits.
The performance of this natural quantum reservoir is demonstrated in a
benchmark time-series regression problem and a practical problem classifying
different objects based on temporal sensor data. In both cases the proposed
reservoir computer shows a higher performance than a linear regression or
classification model. The results indicate that a noisy quantum device
potentially functions as a reservoir computer, and notably, the quantum noise,
which is undesirable in the conventional quantum computation, can be used as a
rich computation resource.
