Higher-Order Quantum Reservoir Computing
- URL: http://arxiv.org/abs/2006.08999v2
- Date: Tue, 20 Oct 2020 14:41:59 GMT
- Title: Higher-Order Quantum Reservoir Computing
- Authors: Quoc Hoan Tran and Kohei Nakajima
- Abstract summary: We propose a hybrid quantum-classical framework consisting of multiple but small quantum systems that are mutually communicated via classical connections like linear feedback.
We demonstrate the effectiveness of our framework in emulating large-scale nonlinear dynamical systems.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Quantum reservoir computing (QRC) is an emerging paradigm for harnessing the
natural dynamics of quantum systems as computational resources that can be used
for temporal machine learning tasks. In the current setup, QRC is difficult to
deal with high-dimensional data and has a major drawback of scalability in
physical implementations. We propose higher-order QRC, a hybrid
quantum-classical framework consisting of multiple but small quantum systems
that are mutually communicated via classical connections like linear feedback.
By utilizing the advantages of both classical and quantum techniques, our
framework enables an efficient implementation to boost the scalability and
performance of QRC. Furthermore, higher-order settings allow us to implement a
FORCE learning or an innate training scheme, which provides flexibility and
high operability to harness high-dimensional quantum dynamics and significantly
extends the application domain of QRC. We demonstrate the effectiveness of our
framework in emulating large-scale nonlinear dynamical systems, including
complex spatiotemporal chaos, which outperforms many of the existing machine
learning techniques in certain situations.
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