Stabilizing quantum GANs

Robin Singh Bhadoria, Vishrut Thakur, Tofigh Allahviranloo

Md. Abdur Rahman, Alfredo Cuzzocrea, Hossain Shahriar

The rapid advancement of Quantum Machine Learning (QML) has introduced new possibilities and challenges in the field of cybersecurity. Generative Adversarial Networks (GANs) have been used as promising tools in Machine Learning (ML) and QML for generating realistic synthetic data from existing (real) dataset which aids in the analysis, detection, and protection against adversarial attacks. In fact, Quantum Generative Adversarial Networks (QGANs) has great ability for numerical data as well as image data generation which have high-dimensional features using the property of quantum superposition. However, effectively loading datasets onto quantum computers encounters significant obstacles due to losses and inherent noise which affects performance. In this work, we study the impact of various losses during training of QGANs as well as GANs for various state-of-the-art cybersecurity datasets. This paper presents a comparative analysis of the stability of loss functions for real datasets as well as GANs generated synthetic dataset. Therefore, we conclude that QGANs demonstrate superior stability and maintain consistently lower generator loss values than traditional machine learning approaches like GANs. Consequently, experimental results indicate that the stability of the loss function is more pronounced for QGANs than GANs.

Xiaoping Lou, Chengzeng Long, L C WANG +1 more

The development of compact and controllable generative models under resource constraints remains a critical challenge for the practical deployment of conditional generative adversarial networks (cGANs). In this work, QuCon-GAN, a hybrid quantum-classical conditional GAN, is proposed to enhance generation expressiveness while drastically reducing parameter counts. Within the generator, dynamic data re-upload, parameterized unitary transformations, and adaptive entanglement have been incorporated into a variational quantum circuit, thereby enabling continuous semantic guidance and feature refinement across layers. When compared to classical counterparts, QuCon-GAN achieves significant improvements in parameter efficiency and conditional control, supporting full-category generation on the MNIST dataset at a resolution of 32×32 pixels. Extensive experiments have demonstrated that QuCon-GAN attains a Fréchet Inception Distance (FID) of 52.42, while maintaining stable adversarial training and avoiding mode collapse even under limited quantum resources (8 qubits). Furthermore, systematic comparisons against classical baselines and recent quantum GANs have validated the effectiveness of QuCon-GAN in balancing resource efficiency, stability, and semantic controllability. This work demonstrates the potential of hybrid quantum-classical architectures for advancing lightweight generative modeling in the Noisy Intermediate-Scale Quantum (NISQ) era.

Satyadhyan Chickerur, Vasavi Kumbargeri

Xinghao Jia, Yi Zheng, Shuang Ren

A. Lumini C Manna, Rajat Subhra Goswami

Abstract Generative Adversarial Networks (GANs) have revolutionized Deep learning-based generative models are still challenging to train. This is due to issues such as mode collapse, instability, and non-convergence. This article provides a comprehensive analysis of the challenges faced by classical GANs, offering insights into their computational flow and limitations. Additionally, it explores Quantum Generative Adversarial Networks (QuGANs), detailing their unique architecture and the mechanism of quantum information generation. To further advance the field, this work presents an in-depth analysis of a hybridized model combining classical GANs and QuGANs, leveraging the strengths of both paradigms to enhance learning and stability. Furthermore, a novel algorithm for training quantum–classical GANs is proposed, with a detailed complexity analysis to assess its computational efficiency. The integration of quantum principles into classical frameworks offers a promising direction to mitigate existing GANs limitations while unlocking new possibilities for quantum machine learning. The proposed approach aims to bridge the gap between classical and quantum generative models, improving training dynamics and sample quality. The objective of this investigation is to establish a foundation for improving the efficiency and comprehension of hybrid GANs architectures, which will allow the design of more efficient and durable generative models in quantum computing.