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Quantum Bootstrap Aggregation

  • David Windridge
  • Rajagopal Nagarajan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10106)

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.

Keywords

Support Vector Machine Training Vector Quantum Superposition Query Oracle Random Subspace Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer Science, School of Science and TechnologyMiddlesex UniversityLondonUK

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