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wesley7137
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quantum

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FIG.4. Demonstrationsofchaoticattractoremulations. (a)Lorenzattractor. (b)Mackey-Glasssystem. (c)Ro¨sslerattractor.
(d) H´enon map. The dotted line shows the time step when the system is switched from teacher forced state to autonomous
state. In the right side, delayed phase diagrams of learned dynamics are shown.
V. CONCLUSION AND DISCUSSION stand the power of a quantum enhanced feature space.
ACKNOWLEDGEMENT
Here we reviewed quantum reservoir computing and
related approaches, quantum extreme learning machine
and quantum circuit learning. The idea of quantum KF is supported by KAKENHI No.16H02211, JST
reservoir computing comes from the spirit of reservoir PRESTO JPMJPR1668, JST ERATO JPMJER1601,
computing, i.e., outsourcing information processing to andJSTCRESTJPMJCR1673. KNissupportedbyJST
natural physical systems. This idea is best suited to PRESTOGrantNumberJPMJPR15E7,Japan,byJSPS
quantum machine learning on near-term quantum de- KAKENHI Grant Numbers JP18H05472, JP16KT0019,
vices in NISQ (noisy intermediate quantum) era. Since and JP15K16076. KN would like to acknowledge Dr.
reservoir computing uses complex physical systems as a Quoc Hoan Tran for his helpful comments. This work is
feature space to construct a model by the simple linear supported by MEXT Quantum Leap Flagship Program
regression, this approach would be a good way to under- (MEXT Q-LEAP) Grant No. JPMXS0118067394.
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