Quantum computing has recently emerged as a transformative technology. Yet, its promised advantages rely on efficiently translating quantum operations into viable physical realizations. Here 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 machine learning 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, both enhancing practical applications and providing insights into theoretical quantum computation.

量子计算最近已成为一项变革性技术。然而，它所承诺的优势依赖于有效地将量子运算转化为可行的物理实现。在这里，我们使用生成机器学习模型，特别是去噪扩散模型（DM）来促进这种转变。利用文本调节，我们引导模型在基于门的量子电路中产生所需的量子操作。值得注意的是，DM 允许在训练过程中回避量子动力学经典模拟中固有的指数开销——这是先前机器学习技术中一直存在的瓶颈。我们展示了该模型在两项任务中的功能：纠缠生成和单一编译。该模型擅长生成新电路，并支持典型的 DM 扩展，例如掩蔽和编辑，以便使电路生成与目标量子器件的约束保持一致。鉴于其灵活性和泛化能力，我们认为 DM 是量子电路合成中的关键，既可以增强实际应用，又可以为理论量子计算提供见解。