  In this paper we investigate the connection between quantum information
theory and machine learning. In particular, we show how quantum state
discrimination can represent a useful tool to address the standard
classification problem in machine learning. Previous studies have shown that
the optimal quantum measurement theory developed in the context of quantum
information theory and quantum communication can inspire a new binary
classification algorithm that can achieve higher inference accuracy for various
datasets. Here we propose a model for arbitrary multiclass classification
inspired by quantum state discrimination, which is enabled by encoding the data
in the space of linear operators on a Hilbert space. While our algorithm is
quantum-inspired, it can be implemented on classical hardware, thereby
permitting immediate applications.
