Scientific Reports (Oct 2021)

Quantum semi-supervised generative adversarial network for enhanced data classification

  • Kouhei Nakaji,
  • Naoki Yamamoto

DOI
https://doi.org/10.1038/s41598-021-98933-6
Journal volume & issue
Vol. 11, no. 1
pp. 1 – 10

Abstract

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Abstract In this paper, we propose the quantum semi-supervised generative adversarial network (qSGAN). The system is composed of a quantum generator and a classical discriminator/classifier (D/C). The goal is to train both the generator and the D/C, so that the latter may get a high classification accuracy for a given dataset. Hence the qSGAN needs neither any data loading nor to generate a pure quantum state, implying that qSGAN is much easier to implement than many existing quantum algorithms. Also the generator can serve as a stronger adversary than a classical one thanks to its rich expressibility, and it is expected to be robust against noise. These advantages are demonstrated in a numerical simulation.