End-to-End QGAN-Based Image Synthesis via Neural Noise Encoding and Intensity Calibration

Abstract

Quantum Generative Adversarial Networks (QGANs) offer a promising path for learning data distributions on near-term quantum devices. However, existing QGANs for image synthesis avoid direct full-image generation, relying on classical post-processing or patch-based methods. These approaches dilute the quantum generator's role and struggle to capture global image semantics. To address this, we propose ReQGAN, an end-to-end framework that synthesizes an entire N=2^D-pixel image using a single D-qubit quantum circuit. ReQGAN overcomes two fundamental bottlenecks hindering direct pixel generation: (1) the rigid classical-to-quantum noise interface and (2) the output mismatch between normalized quantum statistics and the desired pixel-intensity space. We introduce a learnable Neural Noise Encoder for adaptive state preparation and a differentiable Intensity Calibration module to map measurements to a stable, visually meaningful pixel domain. Experiments on MNIST and Fashion-MNIST demonstrate that ReQGAN achieves stable training and effective image synthesis under stringent qubit budgets, with ablation studies verifying the contribution of each component.

Figures (9)

• Figure 1: Common strategies for QGAN-based image synthesis. Left: Low-dimensional generation relies on classical components for dimensionality reduction and reconstruction. Right: Patch-wise generation uses multiple quantum sub-generators for local patches. Both approaches circumvent two key bottlenecks for direct end-to-end generation, which we highlight: ineffective noise injection and the challenge of mapping normalized quantum outputs to pixel intensities.
• Figure 2: Schematic diagram of the overall ReQGAN framework. The generator consists of a Neural Noise Encoder, a Quantum Circuit, and an Intensity Calibration module. The generator first samples a latent vector $\mathbf{a}$ from a uniform distribution and inputs it into the Neural Noise Encoder. This encoder outputs two types of information: a noise vector $\mathbf{z}$ injected into the quantum circuit to drive quantum state preparation, and affine calibration coefficients $(\alpha, \beta)$ used for subsequent intensity calibration. The conditional probability distribution measured from the quantum circuit then enters the Intensity Calibration module, sequentially undergoing amplitude smoothing, deviation modeling, contrast normalization, and adaptive affine projection, to be converted into pixel intensity representations. Finally, the generator and discriminator are alternately updated and jointly optimized under an adversarial training framework to achieve end-to-end pixel-level image generation.
• Figure 3: Schematic of the quantum generator circuit consisting of state preparation $P(\mathbf{z})$ and an $L$-layer parameterized ansatz.
• Figure 4: Visualization of real samples and generated/perturbed fake samples (top row), and the corresponding average FFT spectra before (middle row) and after (bottom row) the Edge-to-RGB completion.
• Figure 5: Generated samples of PQWGAN and ReQGAN across MNIST digit classes (0--9).