Quantum diffusion models provide a physics-consistent route to generative learning by formulating noising and denoising directly on quantum states. However, applying such models to classical high-dimensional data is constrained by the qubit cost of state encoding and the computational burden of simulating large density operators.
arXiv:2401. 07039v5 Announce Type: replace-cross Abstract: Mixed quantum states are the native description of many physically important quantum systems, making their generation a fundamental task in quantum information processing.
By Chuangtao Chen, Qinglin Zhao, MengChu Zhou, Zhimin He, Zhili Sun, Haozhen Situ
arXiv:2603.06755v3 Announce Type: replace
Abstract: We propose a quantum implicit neural representation (QINR)-based autoencoder (AE) and variational autoencoder (VAE) for image reconstruction and ge...
By Saadet M\"uzehher Eren
The paper introduces a scalable hybrid Quantum Diffusion Model for medical image analysis, leveraging a Discrete-Time Quantum Walk executed on a real quantum device to model forward diffusion dynamics. A classical learning model is employed for the backward denoising step, enabling the processing of large real-world medical data, including grayscale, RGB, and moderate-sized 3D volumes. The authors benchmark their quantum approach against a classical discrete-state diffusion model, demonstrating competitive generation performance across three state-of-the-art image generation metrics.
By Francesco Aldo Venturelli, Stefano Martina, Marco Parigi, Filippo Caruso, Alba Cervera-Lierta, Miguel A. Gonz\'alez Ballester
arXiv:2606. 26312v1 Announce Type: cross Abstract: Autoencoders transformed classical machine learning by solving the curse of dimensionality, enabling principled weight initialization and learning compact, structured representations.
By Aldo Lamarre, Dominik \v{S}afr\'anek
arXiv:2603. 00233v2 Announce Type: replace-cross Abstract: Quantum generative modeling is a rapidly evolving discipline at the intersection of quantum computing and machine learning.
By Jonas J\"ager, Florian J. Kiwit, Carlos A. Riofr\'io