Quantum semi-supervised generative adversarial network for enhanced data classification

• URL: http://arxiv.org/abs/2010.13727v1
• Date: Mon, 26 Oct 2020 17:11:49 GMT
• Title: Quantum semi-supervised generative adversarial network for enhanced data classification
• Authors: Kouhei Nakaji and Naoki Yamamoto
• Abstract summary: We propose a quantum semi-supervised generative adversarial network (qSGAN) The system is composed of a quantum generator and a classical discriminator/classifier (D/C)
• Score: 1.1110435360741175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. The generator needs neither any data loading nor to generate a pure quantum state, while it is expected to serve as a stronger adversary than a classical one thanks to its rich expressibility. These advantages are demonstrated in a numerical simulation.

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