Quantum-Inspired Classification Based on Voronoi Tessellation and Pretty-Good Measurements

Abstract

In quantum machine learning, feature vectors are encoded into quantum states. Measurements for the discrimination of states are useful tools for classification problems. Classification algorithms inspired by quantum state discrimination have recently been implemented on classical computers. We present a local approach combining Vonoroi-type tessellation of a training set with pretty-good measurements for quantum state discrimination.

Keywords

• quantum state discrimination
• optimal quantum measurement
• machine learning