2022
DOI: 10.21203/rs.3.rs-2136572/v1
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Variational Quantum Approximate Support Vector Machine With Inference Transfer

Abstract: A kernel-based quantum classifier is the most interesting and powerful quantum machine learning technique for hyperlinear classification of complex data, which can be easily realized in shallow-depth quantum circuits such as a SWAP test classifier. A variational quantum approximate support vector machine (VQASVM) can be realized inherently and explicitly on these circuits by introduction of a variational scheme to map the quadratic optimization problem of the support vector machine theory to a quantum-classica… Show more

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