Quantum reservoir computing: a reservoir approach toward quantum machine learning on near-term quantum devices

• URL: http://arxiv.org/abs/2011.04890v1
• Date: Tue, 10 Nov 2020 04:45:52 GMT
• Title: Quantum reservoir computing: a reservoir approach toward quantum machine learning on near-term quantum devices
• Authors: Keisuke Fujii and Kohei Nakajima
• Abstract summary: Quantum reservoir computing is an approach to use such a complex and rich dynamics on the quantum systems as it is for temporal machine learning. All these quantum machine learning approaches are experimentally feasible and effective on the state-of-the-art quantum devices.
• Score: 0.8206877486958002
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum systems have an exponentially large degree of freedom in the number of particles and hence provide a rich dynamics that could not be simulated on conventional computers. Quantum reservoir computing is an approach to use such a complex and rich dynamics on the quantum systems as it is for temporal machine learning. In this chapter, we explain quantum reservoir computing and related approaches, quantum extreme learning machine and quantum circuit learning, starting from a pedagogical introduction to quantum mechanics and machine learning. All these quantum machine learning approaches are experimentally feasible and effective on the state-of-the-art quantum devices.

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