用扩散模型生成可调量子线路，提升量子计算设计效率。
Leveraging Diffusion Models for Parameterized Quantum Circuit Generation
- 基于去噪扩散模型，同时生成量子线路结构和连续参数。
- 在生成高保真GHZ态和量子机器学习分类任务中表现优异。
- 适用于不同门集和扩展比特数，适合量子算法开发者使用。
量子计算潜力巨大，但其实用性依赖于量子线路设计的进步。本文提出一种基于去噪扩散模型（DMs）的生成方法，用于合成参数化量子电路（PQCs）。在Fürrutter等人[1]近期扩散模型流程基础上，我们的模型有效实现了条件生成，能够同时生成电路架构与连续门参数。我们在生成高保真格林伯格-霍恩-泽利宁格（GHZ）态及实现高精度量子机器学习（QML）分类任务中验证了该方法的有效性。结果表明，该方法在不同门集和扩展比特数下均表现出强泛化能力，凸显了基于扩散模型方法的通用性与计算高效性。本工作展示了生成模型作为加速和优化PQC设计的强大工具，有助于推动更实用、可扩展的量子应用发展。
原文摘要 · Abstract (English)
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 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.
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