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Forecasting chaotic systems is a notably complex task, which in recent yearshas been approached with reasonable success using reservoir computing (RC), arecurrent network with fixed random weights (the reservoir) used to extract thespatio-temporal information of the system. This work presents a hybrid quantumreservoir-computing (HQRC) framework, which replaces the reservoir in RC with aquantum circuit. The modular structure and measurement feedback in the circuitare used to encode the complex system dynamics in the reservoir states, fromwhich classical learning is performed to predict future dynamics. The noiselesssimulations of HQRC demonstrate valid prediction times comparable tostate-of-the-art classical RC models for both the Lorenz63 and double-scrollchaotic paradigmatic systems and adhere to the attractor dynamics long afterthe forecasts have deviated from the ground truth.

Hybrid quantum-classical reservoir computing for simulating chaotic systems