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QuantumReservoirPy: A Software Package for Time Series Prediction

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arxiv 2401.10683 v1 pith:WQ44LSOK submitted 2024-01-19 quant-ph cs.SE

classification quant-phcs.SE
keywords quantumpackagereservoircomputingsoftwarearchitecturespredictionseries
verification ladder T0 review T1 audit T2 compute T3 formal
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In recent times, quantum reservoir computing has emerged as a potential resource for time series prediction. Hence, there is a need for a flexible framework to test quantum circuits as nonlinear dynamical systems. We have developed a software package to allow for quantum reservoirs to fit a common structure, similar to that of reservoirpy which is advertised as "a python tool designed to easily define, train and use (classical) reservoir computing architectures". Our package results in simplified development and logical methods of comparison between quantum reservoir architectures. Examples are provided to demonstrate the resulting simplicity of executing quantum reservoir computing using our software package.

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  1. Input-dependence in quantum reservoir computing

    quant-ph 2024-12 conditional novelty 4.0 of 10

    Quantum reservoir filters are injective if the state update is input-invertible at reachable states, reducible to a rank condition in affine quantum systems.

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