ArticleScientific reports2021
Quantum semi-supervised generative adversarial network for enhanced data classification.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Benchmarking MedMNIST dataset on real quantum hardware.Scientific reports · 2026Article
- Quantum architecture search via truly proximal policy optimization.Scientific reports · 2023Article
- Quantum pixel representations and compression for N-dimensional images.Scientific reports · 2022Article
- Federated Quantum Machine Learning.Entropy (Basel, Switzerland) · 2021Article
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Authors and funding
2 authors.
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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.
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