Efficiently compiling quantum operations remains a major bottleneck in scaling quantum computing. Today's state-of-the-art methods achieve low compilation error by combining search algorithms with gradient-based parameter optimization, but they incur long runtimes and require multiple calls to quantum hardware or expensive classical simulations, making their scaling prohibitive. Recently, machine-learning models have emerged as an alternative, though they are currently restricted to discrete gate sets. Here, we introduce a multimodal denoising diffusion model that simultaneously generates a circuit's structure and its continuous parameters for compiling a target unitary. It leverages two independent diffusion processes, one for discrete gate selection and one for parameter prediction. We benchmark the model over different experiments, analyzing the method's accuracy across varying qubit counts and circuit depths, showcasing the ability of the method to outperform existing approaches in gate counts and under noisy conditions. Additionally, we show that a simple post-optimization scheme allows us to significantly improve the generated ansätze. Finally, by exploiting its rapid circuit generation, we create large datasets of circuits for particular operations and use these to extract valuable heuristics that can help us discover new insights into quantum circuit synthesis.

翻译：高效编译量子操作仍然是扩展量子计算规模的主要瓶颈。当前最先进的方法通过结合搜索算法与基于梯度的参数优化来实现低编译错误率，但这些方法运行时间较长，且需要多次调用量子硬件或进行昂贵的经典模拟，导致其可扩展性受限。近期，机器学习模型作为替代方案逐渐兴起，但目前仅限于离散门集。本文提出了一种多模态去噪扩散模型，该模型能同时生成电路结构及其连续的编译目标幺正参数。该模型利用两个独立的扩散过程：一个用于离散门选择，另一个用于参数预测。我们通过不同实验对模型进行基准测试，分析了在不同量子比特数和电路深度下的方法精度，展示了该方法在门数量和噪声条件下优于现有方案的能力。此外，研究表明简单的后优化方案能显著改进生成的ansätze。最后，利用该模型快速生成电路的特性，我们为特定操作创建了大规模电路数据集，并从中提取有启发性的启发式策略，这有助于发现量子电路综合的新见解。