  Quantum reservoir computing is a machine-learning approach designed to
exploit the dynamics of quantum systems with memory to process information. As
an advantage, it presents the possibility to benefit from the quantum resources
provided by the reservoir combined with a simple and fast training strategy. In
this work, this technique is introduced with a quantum reservoir of spins and
it is applied to find the ground-state energy of an additional quantum system.
The quantum reservoir computer is trained with a linear model to predict the
lowest energy of a particle in the presence of different speckle-disorder
potentials. The performance of the task is analyzed with a focus on the
observable quantities extracted from the reservoir and it shows to be enhanced
when two-qubit correlations are employed.
