  Driven by growing computational power and algorithmic developments, machine
learning methods have become valuable tools for analyzing vast amounts of data.
Simultaneously, the fast technological progress of quantum information
processing suggests employing quantum hardware for machine learning purposes.
Recent works discuss different architectures of quantum perceptrons, but the
abilities of such quantum devices remain debated. Here, we investigate the
storage capacity of a particular quantum perceptron architecture by using
statistical mechanics techniques and connect our analysis to the theory of
classical spin glasses. We focus on a specific quantum perceptron model and
explore its storage properties in the limit of a large number of inputs.
Finally, we comment on using statistical physics techniques for further studies
of neural networks.
