Higher-Order Quantum Reservoir Computing
This work addresses scalability issues in quantum reservoir computing for temporal machine learning tasks, representing an incremental improvement with potential domain-specific applications.
The paper tackles the scalability and high-dimensional data handling limitations of quantum reservoir computing by proposing a higher-order hybrid quantum-classical framework, which demonstrates effectiveness in emulating large-scale nonlinear dynamical systems and outperforms existing machine learning techniques in certain situations.
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.