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Realizing a quantum generative adversarial network using a programmable superconducting processor
- Source :
- npj Quantum Inf 7, 165 (2021)
- Publication Year :
- 2020
-
Abstract
- Generative adversarial networks are an emerging technique with wide applications in machine learning, which have achieved dramatic success in a number of challenging tasks including image and video generation. When equipped with quantum processors, their quantum counterparts--called quantum generative adversarial networks (QGANs)--may even exhibit exponential advantages in certain machine learning applications. Here, we report an experimental implementation of a QGAN using a programmable superconducting processor, in which both the generator and the discriminator are parameterized via layers of single- and multi-qubit quantum gates. The programmed QGAN runs automatically several rounds of adversarial learning with quantum gradients to achieve a Nash equilibrium point, where the generator can replicate data samples that mimic the ones from the training set. Our implementation is promising to scale up to noisy intermediate-scale quantum devices, thus paving the way for experimental explorations of quantum advantages in practical applications with near-term quantum technologies.
- Subjects :
- Quantum Physics
Subjects
Details
- Database :
- arXiv
- Journal :
- npj Quantum Inf 7, 165 (2021)
- Publication Type :
- Report
- Accession number :
- edsarx.2009.12827
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1038/s41534-021-00503-1