Retrieving past quantum features with deep hybrid classical-quantum reservoir computing

Abstract

Machine learning techniques have achieved impressive results in recent years and the possibility of harnessing the power of quantum physics opens new promising avenues to speed up classical learning methods. Rather than viewing classical and quantum approaches as exclusive alternatives, their integration into hybrid designs has gathered increasing interest, as seen in variational quantum algorithms, quantum circuit learning, and kernel methods. Here we introduce deep hybrid classical-quantum reservoir computing for temporal processing of quantum states where information about, for instance, the entanglement or the purity of past input states can be extracted via a single-step measurement. We find that the hybrid setup cascading two reservoirs not only inherits the strengths of both of its constituents but is even more than just the sum of its parts, outperforming comparable non-hybrid alternatives. The quantum layer is within reach of state-of-the-art multimode quantum optical platforms while the classical layer can be implemented in silico.

22 pages, 6 figures

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• Engineering Quantum Reservoirs through Krylov Complexity, Expressivity and Observability
• From Krylov Complexity to Observability: Capturing Phase Space Dimension with Applications in Quantum Reservoir Computing
• Quantum reservoir computing in Jaynes-Cummings models: Nonlinear memory and time-series prediction