Temporal multiplexing with two quantum evolution times raises valid prediction time in a five-qubit hybrid reservoir computer and yields matching optimal parameter regions for two chaotic systems.
Minimal Quantum Reservoirs with Hamiltonian Encoding
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abstract
We investigate a minimal architecture for quantum reservoir computing based on Hamiltonian encoding, in which input data is injected via modulation of system parameters rather than state preparation. This approach circumvents many of the experimental overheads typically associated with quantum machine learning, enabling computation without feedback, memory, or state tomography. We demonstrate that such a minimal quantum reservoir, despite lacking intrinsic memory, can perform nonlinear regression and prediction tasks when augmented with post-processing delay embeddings. Our results provide a conceptually and practically streamlined framework for quantum information processing, offering a clear baseline for future implementations on near-term quantum hardware.
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Forecasting Low-Dimensional Turbulence via Multi-Dimensional Hybrid Quantum Reservoir Computing
Temporal multiplexing with two quantum evolution times raises valid prediction time in a five-qubit hybrid reservoir computer and yields matching optimal parameter regions for two chaotic systems.