Quantum State Discrimination for Supervised Classification
arXiv:2104.00971
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.
21 pages, 3 figures
References in corpus (10)
- Quantum algorithm for solving linear systems of equations
- Quantum support vector machine for big data classification
- Quantum principal component analysis
- Efficient Simulation of Quantum Systems by Quantum Computers
- Quantum state discrimination and its applications
- Reversing quantum dynamics with near-optimal quantum and classical fidelity
- Prediction by linear regression on a quantum computer
- Quantum machine learning for quantum anomaly detection
- Quantum-inspired algorithms in practice
- The theory of the quantum kernel-based binary classifier