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Higher-Order Quantum Reservoir Computing
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Higher-Order Quantum Reservoir Computing
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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.
Forward citations
Cited by 6 Pith papers
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A new protocol for online amplitude-encoded quantum reservoir computing is proposed that uses mid-circuit measurement and reset to implement partial-trace dynamics and indirect measurements for observables.
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A gate-based quantum reservoir computing architecture with injection/memory qubits forecasts multivariate chaotic time series competitively and, on ENSO, hardware noise appears to improve—not degrade—performance.
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Entanglement and Classical Simulability in Quantum Extreme Learning Machines
In QELMs using XX evolution on image data, classification accuracy saturates comparably to Haar-random unitaries once moderate entanglement develops, remaining classically simulable due to limited entanglement spread.
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Harnessing quantum back-action for time-series processing
Indirect measurements in quantum reservoir computing improve execution time scaling, overall performance, and memory capacity over projective measurements and classical feedback methods.
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Iterative Quantum Feature Maps
IQFMs iteratively constructs deep quantum feature maps from shallow circuits via classical augmentation weights and contrastive layer-wise training, outperforming QCNNs on noisy quantum data and matching classical neu...
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Quantum Reservoir Computing: Recent Advances and Future Directions
A comprehensive survey of quantum reservoir computing that proposes a common system model, a memory-architecture taxonomy, and resource-accounting standards, concluding that no broad quantum advantage is currently dem...
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