Abstract
In this article we employ a model open quantum system consisting of two-level
atomic systems coupled to Lorentzian photonic cavities, as an instantiation of
a quantum physical reservoir computer. We then deployed the quantum reservoir
computing approach to an archetypal machine learning problem of image
recognition. We contrast the effectiveness of the quantum physical reservoir
computer against a conventional approach using neural network of the similar
architecture with the quantum physical reservoir computer layer removed.
Remarkably, as the data set size is increased the quantum physical reservoir
computer quickly starts out perform the conventional neural network.
Furthermore, quantum physical reservoir computer provides superior
effectiveness against number of training epochs at a set data set size and
outperformed the neural network approach at every epoch number sampled.
Finally, we have deployed the quantum physical reservoir computer approach to
explore the quantum problem associated with the dynamics of open quantum
systems in which an atomic system ensemble interacts with a structured photonic
reservoir associated with a photonic band gap material. Our results demonstrate
that the quantum physical reservoir computer is equally effective in generating
useful representations for quantum problems, even with limited training data
size.