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Quantum reservoir computing is strongly emerging for sequential and timeseries data prediction in quantum machine learning. We make advancements to thequantum noise-induced reservoir, in which reservoir noise is used as a resourceto generate expressive, nonlinear signals that are efficiently learned with asingle linear output layer. We address the need for quantum reservoir tuningwith a novel and generally applicable approach to quantum circuitparameterization, in which tunable noise models are programmed to the quantumreservoir circuit to be fully controlled for effective optimization. Oursystematic approach also involves reductions in quantum reservoir circuits inthe number of qubits and entanglement scheme complexity. We show that with onlya single noise model and small memory capacities, excellent simulation resultswere obtained on nonlinear benchmarks that include the Mackey-Glass system for100 steps ahead in the challenging chaotic regime.

Optimizing quantum noise-induced reservoir computing for nonlinear and chaotic time series prediction