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• W3206946253 abstract "Here, we focus on more traditional approaches to quantum machine learning which try to speed up classical routines by making use of fault-tolerant quantum computers. We discuss quantum machine learning algorithms based on linear algebra subroutines such as matrix inversion, and those based on amplitude amplification or Grover search. We will then have a look at how classical probabilistic models like Bayesian nets and Boltzmann machines can be implemented on a quantum computer, and finish with an idea of how to use superposition to represent ensembles of classifiers." @default.
• W3206946253 created "2021-10-25" @default.
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• W3206946253 date "2021-01-01" @default.
• W3206946253 modified "2023-09-23" @default.
• W3206946253 title "Fault-Tolerant Quantum Machine Learning" @default.
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• W3206946253 doi "https://doi.org/10.1007/978-3-030-83098-4_7" @default.
• W3206946253 hasPublicationYear "2021" @default.
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