Local Approach to Quantum-inspired Classification

被引：0
|
作者：
Enrico Blanzieri
Roberto Leporini
Davide Pastorello
机构：
[1] University of Trento,Department of Information Engineering and Computer Science
[2] TIFPA-INFN,Department of Economics
[3] University of Bergamo,undefined
来源：
International Journal of Theoretical Physics | / 62卷
关键词：
Quantum state discrimination; k-nearest neighbors; Machine learning;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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