Quantum-inspired classification via efficient simulation of Helstrom measurement

• DOI: 10.48550/arxiv.2403.15308
• Publication Date: 2024-03-22
• Authors: Wooseop Hwang; Daniel K. Park; Israel F. Araujo; Carsten Blank
• Keywords: Quantum Physics; FOS: Physical sciences; Mathematical Physics (math-ph); Quantum Physics (quant-ph); Mathematical Physics

The Helstrom measurement (HM) is known to be the optimal strategy for distinguishing non-orthogonal quantum states with minimum error. Previously, a binary classifier based on classical simulation of HM has been proposed. It was observed that using multiple copies sample data reduced classification Nevertheless, exponential growth in runtime hindered comprehensive investigation relationship between number and performance. We present an efficient method arbitrary by utilizing state fidelity. Our reveals performance does not improve monotonically copies. Instead, it needs treated as hyperparameter subject optimization, achievable only through proposed this work. Quantum-Inspired Machine Learning excellent performance, providing such empirical evidence benchmarking eight datasets comparing 13 optimized standard classifiers.