  Reservoir computing is a novel machine learning algorithm that uses a
nonlinear dynamical system to efficiently learn complex temporal patterns from
data. The objective of this thesis is to investigate the principles of
reservoir computing and develop state-of-the-art variants capable of addressing
diverse applications in machine learning. The research demonstrates the
algorithm's robustness and adaptability across very different domains,
including agricultural time series forecasting and the time propagation of
quantum systems. The first contribution of this thesis consists in developing a
reservoir computing-based methodology to predict future agricultural product
prices, which is crucial for ensuring the sustainability of the food market.
The next contribution of the thesis is devoted to solving the Schr\"odinger
equation for complex quantum systems. A novel reservoir computing framework is
proposed to efficiently propagate quantum wavefunctions in time, enabling the
computation of all eigenstates of a quantum system within a specific energy
range. This approach is used to study prominent systems in the field of quantum
chemistry and quantum chaos. The last contribution of this thesis focuses on
optimizing algorithm designs for quantum reservoir computing. The results
demonstrate that families of quantum circuits with higher complexity, according
to the majorization criterion, yield superior performance in quantum machine
learning. Moreover, the impact of quantum noise on the algorithm performance is
evaluated, revealing that the amplitude damping noise can actually be
beneficial for the performance of quantum reservoir computing, while the
depolarizing and phase damping noise should be prioritized for correction.
Furthermore, the optimal design of quantum reservoirs is employed to construct
a hybrid quantum-classical neural network that tackles a fundamental problem in
drug design.
