Medical image classification is often constrained by limited labeled data, motivating generative augmentation; recently, quantum generative models have been proposed for this purpose, frequently reporting accuracy gains. However, such claims are typically based on single training runs, do not match the parameter budgets of the quantum and classical generators, and do not characterize the data regime in which any benefit appears.
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
Quantum Fidelity Landscape-Guided Prior Calibration for Single-Circuit QGAN Image Generation explores a simpler pixel‑level, end‑to‑end approach to quantum generative adversarial networks (QGANs) that avoids patch‑based decomposition. The authors introduce the Quantum Fidelity Landscape (QFL) as an invariant pairwise‑fidelity structure preserved under shared unitary transformations, and use it to calibrate the quantum prior before adversarial training. Their BasicQGAN framework aligns the prior‑induced QFL with the data‑induced QFL, achieving stable, effective image generation on small‑scale grayscale datasets while requiring fewer qubits and trainable parameters than patch‑based quantum generators.
By Xue Yang, Rigui Zhou, Dax Enshan Koh, Siong Thye Goh, Yitao Tang, ShiZheng Jia, Young-Wook Cho, Hongyu Chen
arXiv:2508. 19857v3 Announce Type: replace Abstract: Many successful families of generative models leverage a low-dimensional latent distribution that is mapped to a data distribution.
By Omar Bacarreza, Thorin Farnsworth, Alexander Makarovskiy, Hugo Wallner, Tessa Hicks, Santiago Sempere-Llagostera, John Price, Robert J. A. Francis-Jones, William R. Clements
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
arXiv:2608.28787v1 Announce Type: new
Abstract: Joint-embedding predictive architectures (JEPAs) have primarily been developed for self-supervised representation learning. Denoising JEPA (D-JEPA) rec...
By Meng Zhou, Wenhao You, Yuxing Chen, Yueying Tian