  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.
