Optimal usage of quantum random access memory in quantum machine learning

Jeongho Bang, Arijit Dutta, Seung-Woo Lee, and Jaewan Kim
Phys. Rev. A 99, 012326 – Published 16 January 2019

Abstract

By considering an unreliable oracle in a query-based model of quantum learning, we present a tradeoff relation between the oracle's reliability and the reusability of the quantum state of the input data. The tradeoff relation manifests as the fundamental upper bound on the reusability. This limitation on the reusability would increase the quantum access to the input data, i.e., the usage of quantum random access memory (qRAM), repeating the preparation of a superposition of large (or big) input data on the query failure. However, it is found that a learner can obtain a correct answer even from an unreliable oracle without any additional usage of qRAM; i.e., the complexity of the qRAM query does not increase even with an unreliable oracle. This is enabled by repeatedly cycling the quantum state of the input data to the upper bound on the reusability.

  • Figure
  • Received 17 September 2018

DOI:https://doi.org/10.1103/PhysRevA.99.012326

©2019 American Physical Society

Physics Subject Headings (PhySH)

  1. Research Areas
Quantum Information, Science & Technology

Authors & Affiliations

Jeongho Bang1,2, Arijit Dutta1, Seung-Woo Lee3,*, and Jaewan Kim1,†

  • 1School of Computational Sciences, Korea Institute for Advanced Study, Seoul 02455, Korea
  • 2Institute of Theoretical Physics and Astrophysics, University of Gdańsk, 80-952 Gdańsk, Poland
  • 3Quantum Universe Center, Korea Institute for Advanced Study, Seoul 02455, Korea

  • *swleego@gmail.com
  • jawan@kias.re.kr

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Vol. 99, Iss. 1 — January 2019

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