Quantum Generative Adversarial Networks in a Silicon Photonic Chip with Maximum Expressibility
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909412491264000 |
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| author | Ma, Haoran Ye, Liao Ruan, Fanjie Zhao, Zichao Li, Maohui Wang, Yuehai Yang, Jianyi |
| author_facet | Ma, Haoran Ye, Liao Ruan, Fanjie Zhao, Zichao Li, Maohui Wang, Yuehai Yang, Jianyi |
| contents | Generative adversarial networks (GANs) have achieved remarkable success with realistic tasks such as creating realistic images, texts, and audio. Combining GANs and quantum computing, quantum GANs are thought to have an exponential advantage over their classical counterparts due to the stronger expressibility of quantum circuits. In this research, a two-qubit silicon quantum photonic chip is created, capable of executing arbitrary controlled-unitary (CU) operations and generating any 2-qubit pure state, thus making it an excellent platform for quantum GANs. To capture complex data patterns, a hybrid generator is proposed to inject nonlinearity into quantum GANs. As a demonstration, three generative tasks, covering both pure quantum versions of GANs (PQ-GAN) and hybrid quantum-classical GANs (HQC-GANs), are successfully carried out on the chip, including high-fidelity single-qubit state learning, classical distributions loading, and compressed image production. The experiment results prove that silicon quantum photonic chips have great potential in generative learning applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_05921 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantum Generative Adversarial Networks in a Silicon Photonic Chip with Maximum Expressibility Ma, Haoran Ye, Liao Ruan, Fanjie Zhao, Zichao Li, Maohui Wang, Yuehai Yang, Jianyi Quantum Physics Generative adversarial networks (GANs) have achieved remarkable success with realistic tasks such as creating realistic images, texts, and audio. Combining GANs and quantum computing, quantum GANs are thought to have an exponential advantage over their classical counterparts due to the stronger expressibility of quantum circuits. In this research, a two-qubit silicon quantum photonic chip is created, capable of executing arbitrary controlled-unitary (CU) operations and generating any 2-qubit pure state, thus making it an excellent platform for quantum GANs. To capture complex data patterns, a hybrid generator is proposed to inject nonlinearity into quantum GANs. As a demonstration, three generative tasks, covering both pure quantum versions of GANs (PQ-GAN) and hybrid quantum-classical GANs (HQC-GANs), are successfully carried out on the chip, including high-fidelity single-qubit state learning, classical distributions loading, and compressed image production. The experiment results prove that silicon quantum photonic chips have great potential in generative learning applications. |
| title | Quantum Generative Adversarial Networks in a Silicon Photonic Chip with Maximum Expressibility |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2404.05921 |