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Hybrid quantum-classical reservoir computing for simulating chaotic systems

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arxiv 2311.14105 v2 pith:G2GC3D6S submitted 2023-11-23 quant-ph

classification quant-ph
keywords reservoirchaoticdynamicssystemscircuitclassicalcomplexcomputing
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Forecasting chaotic systems is a notably complex task, which in recent years has been approached with reasonable success using reservoir computing (RC), a recurrent network with fixed random weights (the reservoir) used to extract the spatio-temporal information of the system. This work presents a hybrid quantum reservoir-computing (HQRC) framework, which replaces the reservoir in RC with a quantum circuit. The modular structure and measurement feedback in the circuit are used to encode the complex system dynamics in the reservoir states, from which classical learning is performed to predict future dynamics. The noiseless simulations of HQRC demonstrate valid prediction times comparable to state-of-the-art classical RC models for both the Lorenz63 and double-scroll chaotic paradigmatic systems and adhere to the attractor dynamics long after the forecasts have deviated from the ground truth.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. An architectural capacity ceiling, not a barren plateau: why a fixed-encoding variational quantum circuit cannot fit the Lorenz-63 attractor

    quant-ph 2026-04 conditional novelty 6.0 of 10

    Fixed-reservoir QRC achieves 81% lower test MSE and 52,000x faster training than variational QPINN on Lorenz chaotic prediction with 4-5 qubits.

  2. Exponential concentration and symmetries in Quantum Reservoir Computing

    quant-ph 2025-05 conditional novelty 6.0 of 10

    Symmetries in the reservoir Hamiltonian prevent exponential concentration of output observables in quantum reservoir computing, enabling scalable time-series processing.

  3. Forecasting Low-Dimensional Turbulence via Multi-Dimensional Hybrid Quantum Reservoir Computing

    quant-ph 2025-09 conditional novelty 5.0 of 10

    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.

  4. Quantum Reservoir Computing: Recent Advances and Future Directions

    quant-ph 2026-07 accept novelty 4.0 of 10

    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...

  5. A Novel Hybrid Quantum Reservoir Computing (nHQRC) for Phase Transition Detection in Non-Equilibrium Dynamical Systems

    quant-ph 2026-07 reject novelty 4.0 of 10

    A hybrid quantum reservoir with entropy/QFI-triggered gating is claimed to reduce trajectory decay by 13% versus an SVR baseline, but its classification accuracy is near chance and its own table shows it is worse than...

  6. Near-term Application Engineering Challenges in Emerging Superconducting Qudit Processors

    quant-ph 2025-06 conditional novelty 2.0 of 10

    A review identifying near-term application opportunities and hardware engineering challenges for transmon-cavity qudit processors, with no new experimental or theoretical result.

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