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Hierarchical quantum classifiers

npj quantum information, 2018-12, Vol.4 (1), p.1-8, Article 65 [Peer Reviewed Journal]

2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2056-6387 ;EISSN: 2056-6387 ;DOI: 10.1038/s41534-018-0116-9

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  • Title:
    Hierarchical quantum classifiers
  • Author: Grant, Edward ; Benedetti, Marcello ; Cao, Shuxiang ; Hallam, Andrew ; Lockhart, Joshua ; Stojevic, Vid ; Green, Andrew G. ; Severini, Simone
  • Subjects: Circuits ; Learning algorithms ; Quantum theory
  • Is Part Of: npj quantum information, 2018-12, Vol.4 (1), p.1-8, Article 65
  • Description: Abstract Quantum circuits with hierarchical structure have been used to perform binary classification of classical data encoded in a quantum state. We demonstrate that more expressive circuits in the same family achieve better accuracy and can be used to classify highly entangled quantum states, for which there is no known efficient classical method. We compare performance for several different parameterizations on two classical machine learning datasets, Iris and MNIST, and on a synthetic dataset of quantum states. Finally, we demonstrate that performance is robust to noise and deploy an Iris dataset classifier on the ibmqx4 quantum computer.
  • Publisher: London: Nature Publishing Group
  • Language: English
  • Identifier: ISSN: 2056-6387
    EISSN: 2056-6387
    DOI: 10.1038/s41534-018-0116-9
  • Source: ROAD
    ProQuest Central
    DOAJ Directory of Open Access Journals
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