Quantum semi-supervised generative adversarial network for enhanced data classification.
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ABSTRACT:
SUBMITTER: Nakaji K
PROVIDER: S-EPMC8490428 | biostudies-literature | 2021 Oct
REPOSITORIES: biostudies-literature
Publications
Quantum semi-supervised generative adversarial network for enhanced data classification.
Nakaji Kouhei K Yamamoto Naoki N
Scientific reports 20211004 1
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 ...[more]
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