Generative quantum machine learning via denoising diffusion probabilistic models

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 \emph{quantum …

architectures attention computer computer vision cs.ai cs.lg deep generative models denoising diffusion diverse enabling generate generative generative models key language language models large language large language models machine machine learning quality quant-ph quantum samples simple tasks technology text text generation via vision