QiT is a Quantum‑Inspired Transformer designed for visual recognition tasks. It replaces quantum neural network concepts with scalable classical operations: angle‑inspired encoding of image tokens, self‑attention over periodic features approximating quantum fidelity kernels, and gated multiplicative emulation of variational circuit interactions. The model achieves competitive performance on image‑classification benchmarks, matching a classical Transformer while avoiding the high runtime costs of simulated quantum models.
By Badri N. Patro, Vijay Agneeswaran
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. 00053v1 Announce Type: cross Abstract: The Discrete Fourier Transform, the Discrete Cosine Transform, and their block-wise variants underpin most deployed image and video codecs.
By Shiwen An, Zhongyi Ni, Huanhai Zhou, Jin-Guo Liu
arXiv:2607. 07754v1 Announce Type: new Abstract: Pattern recognition problems arise in a variety of physical image processing situations, and convolutional neural networks are a popular scheme for the required feature extraction and classification tasks.
By Kumari Jyoti, Rohith Babu, Apoorva D. Patel
arXiv:2608. 19789v1 Announce Type: new Abstract: Developed as a workhorse for classical simulations of quantum algorithms and quantum many-body systems, Tensor Network methods have entered the scientific mainstream in quantum physics.
By Michal A. Sterzel, Marko J. Ran\v{c}i\'c
arXiv:2604. 06135v2 Announce Type: replace-cross Abstract: Efficient data loading remains a bottleneck for near-term quantum machine learning.
By Basil Kyriacou, Viktoria Patapovich, Maniraman Periyasamy, Alexey Melnikov
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
TT‑Net introduces a two‑cut tensor‑train decomposition to replace the per‑channel SVD denoising block in conditional GANs, enabling direct cross‑channel information access. In controlled experiments, TT‑Net outperforms SVD‑Net on PSNR and SSIM for Gaussian, motion blur, and salt‑and‑pepper noise, and surpasses EigenGAN and Pix2pix for Gaussian noise. Training dynamics reveal that TT‑Net’s adversarial loss saturates while reconstruction quality continues to improve, raising questions about the role of the adversarial component.
arXiv:2608. 11884v1 Announce Type: cross Abstract: Quantum generative adversarial networks (QGANs) have attracted increasing attention for image generation using parameterized quantum circuits.
By Xue Yang, Rigui Zhou, ShiZheng Jia, Dax Enshan Koh, Siong Thye Goh, Young-Wook Cho, YaoChong Li, Xuezhi Ma, Hongyu Chen, Xin Wang
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.
arXiv:2609.00870v1 Announce Type: cross
Abstract: Tensor networks, originally developed for quantum many-body physics, are promising models for machine learning. We derive stochastic Riemannian optim...
By Marius Willner, Maximilian Scharf, Andr\'e Uschmajew, Timo Felser, Marco Trenti
arXiv:2510. 12430v2 Announce Type: replace-cross Abstract: Translating a general quantum circuit on a specific hardware topology with a reduced set of available gates, also known as transpilation, comes with a substantial increase in the length of the equivalent circuit.
By Bodo Rosenhahn, Tobias J. Osborne, Christoph Hirche