Quantum Generative Adversarial Networks in a Silicon Photonic Chip with Maximum Expressibility

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$C\hat{U}$) operations and generating any two-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-GANs) 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. Quantum generative adversarial networks (GANs) may offer exponential advantages over classical versions due to their stronger circuit expressibility. This work introduces a two-photon generator with maximum expressibility, utilizing a two-qubit silicon photonic chip for three generative tasks, covering pure quantum GANs and hybrid quantum-classical GANs. Experiment results demonstrate the potential of silicon photonic chips in generative learning applications. image