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- W3156223887 abstract "We narrow the gap between previous literature on quantum linear algebra and useful data analysis on a quantum computer, providing quantum procedures that speed-up the solution of eigenproblems for data representation in machine learning. The power and practical use of these subroutines is shown through new quantum algorithms, sublinear in the input matrix's size, for principal component analysis, correspondence analysis, and latent semantic analysis. We provide a theoretical analysis of the run-time and prove tight bounds on the randomized algorithms' error. We run experiments on multiple datasets, simulating PCA's dimensionality reduction for image classification with the novel routines. The results show that the run-time parameters that do not depend on the input's size are reasonable and that the error on the computed model is small, allowing for competitive classification performances." @default.
- W3156223887 created "2021-04-26" @default.
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- W3156223887 date "2021-04-19" @default.
- W3156223887 modified "2023-09-28" @default.
- W3156223887 title "Quantum Algorithms for Data Representation and Analysis." @default.
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