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• W2981621709 abstract "In this work, we present a quantum neighborhood preserving embedding and a quantum local discriminant embedding for dimensionality reduction and classification. We demonstrate that these two algorithms have an exponential speedup over their respectively classical counterparts. Along the way, we propose a variational quantum generalized eigenvalue solver that finds the generalized eigenvalues and eigenstates of a matrix pencil $(mathcal{G},mathcal{S})$. As a proof of principle, we implement our algorithm to solve ${2}^{5}ifmmodetimeselsetexttimesfi{}{2}^{5}$ generalized eigenvalue problems. Finally, our results offer two optional outputs with quantum or classical form, which can be directly applied in another quantum or classical machine learning process." @default.
• W2981621709 created "2019-11-01" @default.
• W2981621709 creator A5021384155 @default.
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• W2981621709 date "2020-03-16" @default.
• W2981621709 modified "2023-10-15" @default.
• W2981621709 title "Variational quantum algorithms for dimensionality reduction and classification" @default.
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• W2981621709 doi "https://doi.org/10.1103/physreva.101.032323" @default.
• W2981621709 hasPublicationYear "2020" @default.
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