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

TL;DR

This paper demonstrates a silicon photonic chip capable of executing quantum GANs, showing promising results in generating complex data and highlighting the potential of quantum photonics in generative learning.

Contribution

It introduces a two-qubit silicon quantum photonic chip for quantum GANs and demonstrates hybrid quantum-classical generative tasks on this platform.

Findings

Successful generation of high-fidelity single-qubit states

Loading classical distributions effectively

Producing compressed images with quantum photonics

Abstract

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.