Abstract
Generative AI is one of the modern humankind's most transformative fields, paving a path for machines to generate realistic information in many forms (e.g., images/text/audio/video/scientific data). Diffusion models are the system that recently showed how to learn to generate them step by step by reversing stochastic noise processes through normalisation layers. Such models outperformed previous techniques including Generative Adversarial Networks (GANs) in terms of stability, diversity and controllability. While, despite those successes, classical diffusion models have major limitations such as expensive computations, slow sampling times or great training pipeline requirements and some challenges when modelling ultra-high-dimensional probability distributions. Emerging computational paradigms to accelerate and enhance generative intelligence systems have come when data complexity is becoming more challenging in domains like healthcare, finance, cybersecurity, climate science, industrial automation. One of the main solutions being offered by quantum computing, which takes advantage of quantum mechanical principles exploits superposition, entanglement and quantum parallelism to process information in a fundamentally new way.
QEDMs, or Quantum-Enhanced Diffusion Models, specify a hybrid architecture that integrates classical diffusion architectures with quantum circuits, quantum kernels or variation quantum neural networks to improve learning efficiency and quality of the generated samples. Quantum processors can be included in these models as part of their noise estimates, latent representation learning, optimization routines or probabilistic sampling stages. This integration can lead to less-complex models, faster convergence, better feature extraction and more informative multimodal outputs. In addition, one could potential zing QEDMs for simulating molecular data, material discovery, optimizing synthetic dataset and privacy conserving data generation via quantum randomness.
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