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.

量子计算中的生成机器学习模型和去噪扩散模型在产生期望的量子操作方面具有优势，并在纠缠生成和酉编译等任务中展示出灵活性和潜力。