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. 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.
arXiv:2510. 03389v2 Announce Type: replace-cross Abstract: Current quantum computers require algorithms that use limited resources economically.
arXiv:2604. 06135v2 Announce Type: replace-cross Abstract: Efficient data loading remains a bottleneck for near-term quantum machine learning.
arXiv:2607. 21186v1 Announce Type: cross Abstract: Numerous studies have analyzed the use of hybrid quantum-classical convolutional neural networks as a promising alternative to classical deep learning.
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.
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.
arXiv:2608. 05595v1 Announce Type: cross Abstract: Circuit cutting lets a large quantum neural network (QNN) run as independent subcircuits on small devices, but rebuilding its outputs by reconstruction carries a classical sampling overhead exponential in the number of cuts - the dominant runtime cost in prior work.
arXiv:2609. 17298v1 Announce Type: cross Abstract: This work introduces quantum-inspired tensor-network circuits as trainable transforms for image inpainting.
Circuit cutting lets a large quantum neural network (QNN) run as independent subcircuits on small devices, but rebuilding its outputs by reconstruction carries a classical sampling overhead exponential in the number of cuts - the dominant runtime cost in prior work. We ask whether, for machine-learning tasks, this step is necessary, and replace it with late fusion: each subcircuit is trained and measured independently, and a small classical head combines their outputs - a linear-cost, decision-level combination borrowed from multimodal learning.
arXiv:2606. 20504v1 Announce Type: cross Abstract: We present a systematic study of von Neumann entropy estimation in multi-qutrit quantum systems using two complementary approaches: variational quantum algorithms (VQAs) and classical convolutional neural networks (CNNs), evaluated using an ideal (noise-free) quantum simulator.
arXiv:2604. 10933v2 Announce Type: replace-cross Abstract: Deep neural networks remain highly vulnerable to adversarial perturbations, limiting their reliability in security- and safety-critical applications.
arXiv:2605. 27923v2 Announce Type: replace-cross Abstract: The rapid growth of computer vision and increasingly complex image recognition tasks has exposed fundamental computational limitations of classical machine learning models, motivating the exploration of quantum computing as an emerging new paradigm.