Leveraging Diffusion Models for Parameterized Quantum Circuit Generation

• URL: http://arxiv.org/abs/2505.20863v3
• Date: Wed, 23 Jul 2025 13:04:46 GMT
• Title: Leveraging Diffusion Models for Parameterized Quantum Circuit Generation
• Authors: Daniel Barta, Darya Martyniuk, Johannes Jung, Adrian Paschke,
• Abstract summary: We introduce a generative approach based on denoising diffusion models (DMs) to synthesize quantum circuits (PQCs)<n>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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum 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 F\"urrutter 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.

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