Deep generative models are key-enabling technology to computer vision, text generation and large language models. Denoising diffusion probabilistic models (DDPMs) have recently gained much attention due to their ability to generate diverse and high-quality samples in many computer vision tasks, as well as to incorporate flexible model architectures and relatively simple training scheme. Quantum generative models, empowered by entanglement and superposition, have brought new insight to learning classical and quantum data. Inspired by the classical counterpart, we propose the quantum denoising diffusion probabilistic models (QuDDPM) to enable efficiently trainable generative learning of quantum data. QuDDPM adopts sufficient layers of circuits to guarantee expressivity, while introduces multiple intermediate training tasks as interpolation between the target distribution and noise to avoid barren plateau and guarantee efficient training. We demonstrate QuDDPM's capability in learning correlated quantum noise model and learning topological structure of nontrivial distribution of quantum data.

翻译：深度生成模型是计算机视觉、文本生成和大语言模型的关键使能技术。去噪扩散概率模型（DDPMs）因其在许多计算机视觉任务中生成多样且高质量样本的能力，以及灵活的模型架构和相对简单的训练方案，近期受到广泛关注。借助纠缠和叠加原理的量子生成模型为学习经典与量子数据带来了新见解。受经典对应方法的启发，我们提出量子去噪扩散概率模型（QuDDPM），以实现量子数据的高效可训练生成式学习。QuDDPM采用足够层数的电路以保证表达力，同时引入多个中间训练任务作为目标分布与噪声之间的插值，从而避免贫瘠高原并保证高效训练。我们展示了QuDDPM在学习相关量子噪声模型以及学习非平凡量子数据分布拓扑结构方面的能力。