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- W3089238086 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." @default.
- W3089238086 created "2020-10-01" @default.
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- W3089238086 date "2020-06-23" @default.
- W3089238086 modified "2023-09-27" @default.
- W3089238086 title "Entanglement assisted training algorithm for supervised quantum classifiers" @default.
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- W3089238086 hasPublicationYear "2020" @default.
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