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Fault-Tolerant Quantum Machine Learning

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Machine Learning with Quantum Computers

Part of the book series: Quantum Science and Technology ((QST))

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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.

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Notes

  1. 1.

    An assumption that allows us to omit that the bounds also depend on the norm.

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Authors and Affiliations

  1. Xanadu Quantum Computing Inc., Toronto, ON, Canada

    Maria Schuld

  2. School of Chemistry and Physics, University of KwaZulu-Natal, Durban, South Africa

    Francesco Petruccione

Authors
  1. Maria Schuld
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  2. Francesco Petruccione
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Correspondence to Maria Schuld .

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Schuld, M., Petruccione, F. (2021). Fault-Tolerant Quantum Machine Learning. In: Machine Learning with Quantum Computers. Quantum Science and Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-83098-4_7

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