  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.
