arXiv:2607. 07072v1 Announce Type: new Abstract: Quantum diffusion models provide a physics-consistent route to generative learning by formulating noising and denoising directly on quantum states.
By Qipeng Qian, Keli Deng, Yuntao Qian
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: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
arXiv:2606. 27561v1 Announce Type: new Abstract: Generative models have achieved remarkable success in data synthesis, though recent advances driven by increasing model scale have introduced challenges in computational cost and efficiency.
By Jack Waller, Filippo Caruso, Dimitrios Makris, Rajagopal Nilavalan, Xing Liang
Quantum generative adversarial networks (QGANs) have attracted increasing attention for image generation using parameterized quantum circuits. Existing amplitude-based approaches face two key limitations: pixel locations are typically encoded by computational-basis indices or address qubits, causing quantum resources to grow with image resolution; meanwhile, jointly decoding many pixels from normalized quantum states introduces probability competition among pixels and limits precise pixel-wise control.