Support Vector Machines with Quantum State Discrimination

We analyze possible connections between quantum-inspired classifications and support vector machines. Quantum state discrimination and optimal quantum measurement are useful tools for classification problems. In order to use these tools, feature vectors have to be encoded in quantum states represent...

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Bibliographic Details
Published in:Quantum reports 2021, Vol.3 (3), p.482-499
Main Authors: Leporini, Roberto, Pastorello, Davide
Format: Article
Language:English
Subjects:
Algorithms
Classification
Discrimination
Hilbert space
Machine learning
Neural networks
optimal quantum measurement
Principal components analysis
Quantum computing
quantum state discrimination
Random access memory
support vector machine
Support vector machines
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Summary:We analyze possible connections between quantum-inspired classifications and support vector machines. Quantum state discrimination and optimal quantum measurement are useful tools for classification problems. In order to use these tools, feature vectors have to be encoded in quantum states represented by density operators. Classification algorithms inspired by quantum state discrimination and implemented on classic computers have been recently proposed. We focus on the implementation of a known quantum-inspired classifier based on Helstrom state discrimination showing its connection with support vector machines and how to make the classification more efficient in terms of space and time acting on quantum encoding. In some cases, traditional methods provide better results. Moreover, we discuss the quantum-inspired nearest mean classification.
ISSN:2624-960X
2624-960X
DOI:10.3390/quantum3030032