Quantum Generative Adversarial Network Enhancement Method Based on Quantum Principal Component Analysis

Quantum machine learning (QML) has attracted increasing attention due to its excellent performance on clas-sification and identification tasks. In quantum machine learning methods, quantum generative adversarial networks (QGANs) are an extension of classical generative adversarial networks in the field of quantum computing. However, current quan-tum generation techniques are limited by quantum computing resources, these techniques are only validated on small-scale datasets, and the images generated by QGANs are of lower quality. To improve the image quality generated by QGANs, we introduce a new method to construct latent spaces based on quantum principal component analysis, to ensure high-quality data output of QGANs. In addition, we employ a parameterized quantum circuit instead of a sub module of quantum principal component analysis (Quantum Phase Estimation) to reduce the circuit depths and the number of gates. Experimental results indicate that our new method, which utilizes the new latent space as the input for QGANs, outperforms the original QGAN in terms of performance metrics Frechet Inception Distance and Cosine similarity. Specifically, our method achieves a 20 % lower Frechet Inception Distance score and 9 % higher cosine similarity compared to the original QGAN.