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.
Original language | English |
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Article number | 19649 |
Journal | Scientific reports |
Volume | 11 |
Issue number | 1 |
DOIs | |
Publication status | Published - 2021 Dec |
ASJC Scopus subject areas
- General