Robust quantum classifier with minimal overhead

Daniel K. Park, Carsten Blank, Francesco Petruccione

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


To witness quantum advantages in practical settings, substantial efforts are required not only at the hardware level but also on theoretical research to reduce the computational cost of a given protocol. Quantum computation has the potential to significantly enhance existing classical machine learning methods, and several quantum algorithms for binary classification based on the kernel method have been proposed. These algorithms rely on estimating an expectation value, which in turn requires an expensive quantum data encoding procedure to be repeated many times. In this work, we calculate explicitly the number of repetition necessary for acquiring a fixed success probability and show that the Hadamard-test and the swap-test circuits achieve the optimal variance in terms of the quantum circuit parameters. The variance, and hence the number of repetition, can be further reduced only via optimization over data-related parameters. We also show that the kernel-based binary classification can be performed with a single-qubit measurement regardless of the number and the dimension of the data. Finally, we show that for a number of relevant noise models the classification can be performed reliably without quantum error correction. Our findings are useful for designing quantum classification experiments under limited resources, which is the common challenge in the noisy intermediate-scale quantum era.

Original languageEnglish
Title of host publicationIJCNN 2021 - International Joint Conference on Neural Networks, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780738133669
Publication statusPublished - 2021 Jul 18
Event2021 International Joint Conference on Neural Networks, IJCNN 2021 - Virtual, Shenzhen, China
Duration: 2021 Jul 182021 Jul 22

Publication series

NameProceedings of the International Joint Conference on Neural Networks


Conference2021 International Joint Conference on Neural Networks, IJCNN 2021
CityVirtual, Shenzhen

Bibliographical note

Funding Information:
This research is supported by the National Research Foundation of Korea (Grant No. 2019R1I1A1A01050161), Quantum Computing Development Program (Grant No. 2019M3E4A1080227), Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF-2021M3H3A1038085), and the South African Research Chair Initiative of the Department of Science and Technology and the National Research Foundation.

Publisher Copyright:
© 2021 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence


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