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.
The paper introduces Tensor Network-based Quantum Data Encoding (TNQE), a framework that uses structured unitary tensor network representations to encode classical data into quantum states efficiently. TNQE decomposes each input into tensor cores and compiles them into encoding circuits via two core-to-circuit strategies, with learnable block unitaries ensuring trainability while maintaining unitarity. The method achieves significantly shallower circuits—down to 0.04× the depth of amplitude encoding—and scales to high‑resolution images, demonstrating practical feasibility on real quantum hardware.
By Guang Lin, Toshihisa Tanaka, Qibin Zhao
arXiv:2510. 03389v2 Announce Type: replace-cross Abstract: Current quantum computers require algorithms that use limited resources economically.
By Jonas J\"ager, Philipp Els\"asser, Elham Torabian
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.
By Guillermo Rubi\~nos Rodr\'iguez, Mart\'in Ottavianelli, Mateo Alonso, Gonzalo Bl\'azquez Gil, Boris-Stephan Rauchmann, Pablo D\'iez-Valle, Sergio Altares-L\'opez
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