Quantum State Discrimination for Supervised Classification
Abstract
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.
Cite
@article{arxiv.2104.00971,
title = {Quantum State Discrimination for Supervised Classification},
author = {Roberto Giuntini and Hector Freytes and Daniel K. Park and Carsten Blank and Federico Holik and Keng Loon Chow and Giuseppe Sergioli},
journal= {arXiv preprint arXiv:2104.00971},
year = {2021}
}
Comments
21 pages, 3 figures