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Quantum Bootstrap Aggregation

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Quantum Interaction (QI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10106))

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Abstract

We set out a strategy for quantizing attribute bootstrap aggregation to enable variance-resilient quantum machine learning. To do so, we utilise the linear decomposability of decision boundary parameters in the Rebentrost et al. Support Vector Machine to guarantee that stochastic measurement of the output quantum state will give rise to an ensemble decision without destroying the superposition over projective feature subsets induced within the chosen SVM implementation. We achieve a linear performance advantage, O(d), in addition to the existing O(log(n)) advantages of quantization as applied to Support Vector Machines. The approach extends to any form of quantum learning giving rise to linear decision boundaries.

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Acknowledgment

The first author would like to acknowledge financial support from the Horizon 2020 European Research project DREAMS4CARS (#731593). The second author is partially supported by EU ICT COST Action IC1405 “Reversible Computation—Extending Horizons of Computing”.

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Authors and Affiliations

  1. Department of Computer Science, School of Science and Technology, Middlesex University, London, NW4 4BT, UK

    David Windridge & Rajagopal Nagarajan

Authors
  1. David Windridge
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  2. Rajagopal Nagarajan
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Correspondence to David Windridge .

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Editors and Affiliations

  1. San Francisco State University, San Francisco, California, USA

    Jose Acacio de Barros

  2. University of Oxford, Oxford, United Kingdom

    Bob Coecke

  3. City University of London, London, United Kingdom

    Emmanuel Pothos

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Windridge, D., Nagarajan, R. (2017). Quantum Bootstrap Aggregation. In: de Barros, J., Coecke, B., Pothos, E. (eds) Quantum Interaction. QI 2016. Lecture Notes in Computer Science(), vol 10106. Springer, Cham. https://doi.org/10.1007/978-3-319-52289-0_9

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  • DOI: https://doi.org/10.1007/978-3-319-52289-0_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52288-3

  • Online ISBN: 978-3-319-52289-0

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