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.
Local Approach to Quantum-inspired Classification / Blanzieri, E.; Leporini, R.; Pastorello, D.. - In: INTERNATIONAL JOURNAL OF THEORETICAL PHYSICS. - ISSN 1572-9575. - 62:1(2023). [10.1007/s10773-022-05263-y]
Local Approach to Quantum-inspired Classification
Blanzieri E.;Pastorello D.
2023-01-01
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 in questo prodotto:
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