Barta, DanielDanielBartaMartyniuk, DaryaDaryaMartyniukJung, JohannesJohannesJungPaschke, AdrianAdrianPaschke2025-07-222025-07-222025-05-27https://publica.fraunhofer.de/handle/publica/48979410.48550/arXiv.2505.20863Quantum computing holds immense potential, yet its practical success depends on multiple factors, including advances in quantum circuit design. In this paper, we introduce a generative approach based on denoising diffusion models (DMs) to synthesize parameterized quantum circuits (PQCs). Extending the recent diffusion model pipeline of Fürrutter et al. [1], our model effectively conditions the synthesis process, enabling the simultaneous generation of circuit architectures and their continuous gate parameters. We demonstrate our approach in synthesizing PQCs optimized for generating high-fidelity Greenberger-Horne-Zeilinger (GHZ) states and achieving high accuracy in quantum machine learning (QML) classification tasks. Our results indicate a strong generalization across varying gate sets and scaling qubit counts, highlighting the versatility and computational efficiency of diffusion-based methods. This work illustrates the potential of generative models as a powerful tool for accelerating and optimizing the design of PQCs, supporting the development of more practical and scalable quantum applications.enQuantum Circuit SynthesisParameterized Quantum CircuitsGenerative ModelsQuantum ComputingQuantum Architecture SearchDiffusion Modelspaper