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

TL;DR

This paper advances quantum reservoir computing by using tunable noise models and circuit simplifications to improve nonlinear and chaotic time series prediction, demonstrating high performance with minimal resources.

Contribution

Introduces a novel approach to quantum reservoir tuning using programmable noise models and reduces circuit complexity for better time series prediction.

Findings

Achieved high accuracy on Mackey-Glass chaotic system prediction.

Demonstrated effective reservoir optimization with minimal qubits and noise models.

Showed that simplified circuits can still perform well on complex benchmarks.

Abstract

Quantum reservoir computing is strongly emerging for sequential and time series data prediction in quantum machine learning. We make advancements to the quantum noise-induced reservoir, in which reservoir noise is used as a resource to generate expressive, nonlinear signals that are efficiently learned with a single linear output layer. We address the need for quantum reservoir tuning with a novel and generally applicable approach to quantum circuit parameterization, in which tunable noise models are programmed to the quantum reservoir circuit to be fully controlled for effective optimization. Our systematic approach also involves reductions in quantum reservoir circuits in the number of qubits and entanglement scheme complexity. We show that with only a single noise model and small memory capacities, excellent simulation results were obtained on nonlinear benchmarks that include the Mackey-Glass system for 100 steps ahead in the challenging chaotic regime.