基于多模态扩散模型的离散-连续量子电路综合

Synthesis of discrete-continuous quantum circuits with multimodal diffusion models

June 2, 2025

作者: Florian Fürrutter, Zohim Chandani, Ikko Hamamura, Hans J. Briegel, Gorka Muñoz-Gil

cs.AI

摘要

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将搜索算法与基于梯度的参数优化相结合，实现了较低的编译误差，但这些方法耗时长，且需要多次调用量子硬件或昂贵的经典模拟，使其扩展性受到限制。最近，机器学习模型作为一种替代方案出现，尽管目前它们仅限于离散门集。在此，我们提出了一种多模态去噪扩散模型，该模型能够同时生成电路结构及其连续参数，以编译目标酉矩阵。它利用了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。我们在不同实验中对该模型进行了基准测试，分析了该方法在不同量子比特数量、电路深度和参数化门比例下的准确性。最后，通过利用其快速生成电路的能力，我们创建了特定操作的大规模电路数据集，并利用这些数据集提取出有价值的启发式信息，这些信息有助于我们在量子电路合成领域获得新的洞见。

English

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, circuit depths, and proportions of parameterized gates. 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.