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Variational Circuits as Machine Learning Models

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Machine Learning with Quantum Computers

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

Abstract

We explain how parametrised quantum circuits—quantum algorithms that are popular in near-term quantum computing—can be used as machine learning models, and review techniques to analyse and train such quantum models in a deep-learning fashion, including measures of expressivity and trainability, as well as parameter-shift rules.

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Notes

  1. 1.

    This is the same reason why classical machine learning can use one data sample in each step of stochastic gradient descent.

  2. 2.

    Such an angle encoded qubit has been called a quron [44] in the context of quantum neural networks.

  3. 3.

    Thanks to Gian Giacomo Guerrschi for this simplified presentation.

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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

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  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). Variational Circuits as Machine Learning Models. In: Machine Learning with Quantum Computers. Quantum Science and Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-83098-4_5

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