In the context of quantum-inspired machine learning, remarkable mathematical tools for solving classification problems are given by some methods of quantum state discrimination. In this respect, quantum-inspired classifiers based on nearest centroid and Helstrom discrimination have been efficiently implemented on classical computers. We present a local approach combining the kNN algorithm to some quantum-inspired classifiers.
Enrico Blanzieri, Roberto Leporini, Davide Pastorello (2022). Local Approach to Quantum-inspired Classification. INTERNATIONAL JOURNAL OF THEORETICAL PHYSICS, 62(1), 1-10 [10.1007/s10773-022-05263-y].
Local Approach to Quantum-inspired Classification
Davide Pastorello
2022
Abstract
In the context of quantum-inspired machine learning, remarkable mathematical tools for solving classification problems are given by some methods of quantum state discrimination. In this respect, quantum-inspired classifiers based on nearest centroid and Helstrom discrimination have been efficiently implemented on classical computers. We present a local approach combining the kNN algorithm to some quantum-inspired classifiers.| File | Dimensione | Formato | |
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