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Quantum Information Processing
Erschienen in: Quantum Information Processing 8/2021

01.08.2021

Entanglement assisted training algorithm for supervised quantum classifiers

verfasst von: Soumik Adhikary

Erschienen in: Quantum Information Processing | Ausgabe 8/2021

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Abstract

We propose a new training algorithm for supervised quantum classifiers. Here, we have harnessed the property of quantum entanglement to build a model that can simultaneously manipulate multiple training samples along with their labels. Subsequently, a Bell-inequality-based cost function is constructed, that can encode errors from multiple samples, simultaneously, in a way that is not possible by any classical means. We show that upon minimizing this cost function one can achieve successful classification in benchmark datasets. The results presented in this paper are for binary classification problems. Nevertheless, the analysis can be extended to multi-class classification problems as well.
Vorheriger Artikel Convex geometry of Markovian Lindblad dynamics and witnessing non-Markovianity
Nächster Artikel The decompositions of Werner and isotropic states

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Metadaten
Titel
Entanglement assisted training algorithm for supervised quantum classifiers
verfasst von
Soumik Adhikary
Publikationsdatum
01.08.2021
Verlag
Springer US
Erschienen in
Quantum Information Processing / Ausgabe 8/2021
Print ISSN: 1570-0755
Elektronische ISSN: 1573-1332
DOI
https://doi.org/10.1007/s11128-021-03179-w

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