Quantum semi-supervised generative adversarial network for enhanced data classification

Kouhei Nakaji, Naoki Yamamoto

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


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.

Original languageEnglish
Article number19649
JournalScientific reports
Issue number1
Publication statusPublished - 2021 Dec

ASJC Scopus subject areas

  • General


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