Designing Effective Quantum Generators: A Comparative Study of Variational Ansätze in Hybrid QGANs
Conference
Tripathi, S, Etar, A, Soni, J et al. (2026). Designing Effective Quantum Generators: A Comparative Study of Variational Ansätze in Hybrid QGANs
. 10.1109/ICAIC67076.2026.11395674
Tripathi, S, Etar, A, Soni, J et al. (2026). Designing Effective Quantum Generators: A Comparative Study of Variational Ansätze in Hybrid QGANs
. 10.1109/ICAIC67076.2026.11395674
Quantum Generative Adversarial Networks (QGANs) combine quantum circuit based generative models with classical discriminators to exploit quantum advantages in generative modeling. This work investigates a hybrid QGAN architecture with a quantum-enhanced generator and a classical discriminator, applied to the Modified National Institute of Standards and Technology (MNIST) dataset. We implement and benchmark three different quantum circuit ansätze - namely a basic entangler circuit, a simple two-design circuit and a strongly entangling circuit, with each QGAN model being constructed using six layers of the respective ansätze. To manage the 28x28 image complexity, the generator employs a patch wise scheme, dividing each image into small patches generated by separate sub-circuits. The performance of each ansatz is evaluated in terms of training stability and output image quality. Our results demonstrate that the choice of quantum circuit ansatz significantly impacts the QGAN's ability to learn the distribution of handwritten digits. In particular, the strongly entangling ansatz achieves the best performance at the cost of stability and image fidelity among the three yielding recognizable digit images. While no quantum advantage was demonstrated, this study provides comparative analysis and insights into designing effective quantum generators for hybrid QGANs on image data, serving as a benchmark for future developments in quantum-enhanced generative models.
