Quantum Bootstrap Aggregation

Windridge, David ORCID logoORCID: https://orcid.org/0000-0001-5507-8516 and Nagarajan, Rajagopal ORCID logoORCID: https://orcid.org/0000-0002-9724-4962 (2017) Quantum Bootstrap Aggregation. Quantum Interaction: 10th International Conference, QI 2016, San Francisco, CA, USA, July 20-22, 2016, Revised Selected Papers. In: Quantum Interaction, 20-22 Jul 2016, San Francisco, CA, USA. ISBN 9783319-522883. ISSN 0302-9743 [Conference or Workshop Item] (doi:10.1007/978-3-319-52289-0_9)

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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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published as: 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
Research Areas: A. > School of Science and Technology > Computer Science > Foundations of Computing group
Item ID: 21323
Notes on copyright: The final publication is available at http://link.springer.com/book/10.1007/978-3-319-52289-0
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Depositing User: David Windridge
Date Deposited: 15 Feb 2017 16:26
Last Modified: 29 Nov 2022 21:16
URI: https://eprints.mdx.ac.uk/id/eprint/21323

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