Quantum computing has recently emerged as a transformative technology. Yet, its promised advantages rely on efficiently translating quantum operations into viable physical realizations. In this work, we use generative machine learning models, specifically denoising diffusion models (DMs), to facilitate this transformation. Leveraging text-conditioning, we steer the model to produce desired quantum operations within gate-based quantum circuits. Notably, DMs allow to sidestep during training the exponential overhead inherent in the classical simulation of quantum dynamics -- a consistent bottleneck in preceding ML techniques. We demonstrate the model's capabilities across two tasks: entanglement generation and unitary compilation. The model excels at generating new circuits and supports typical DM extensions such as masking and editing to, for instance, align the circuit generation to the constraints of the targeted quantum device. Given their flexibility and generalization abilities, we envision DMs as pivotal in quantum circuit synthesis, enhancing both practical applications but also insights into theoretical quantum computation.

翻译：量子计算近期已成为一项具有变革性的技术，但其承诺的优势依赖于将量子操作高效地转化为可行的物理实现。本研究利用生成式机器学习模型，特别是去噪扩散模型（DMs），来促进这一转化。通过文本条件控制，我们引导模型在基于门的量子电路中生成所需的量子操作。值得注意的是，DMs允许在训练过程中规避量子动力学经典模拟中固有的指数级开销——这是先前机器学习技术中始终存在的瓶颈。我们在两个任务上展示了模型的能力：纠缠生成和酉编译。该模型擅长生成新电路，并支持典型的DM扩展功能，如掩码与编辑，例如使电路生成与目标量子器件的约束条件对齐。鉴于其灵活性与泛化能力，我们预见DMs将在量子电路合成中发挥关键作用，不仅增强实际应用，还能深化对理论量子计算的理解。