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.
By Sudip Vhaduri, Ryan Gammon, Sayanton Dibbo
arXiv:2608. 04379v1 Announce Type: cross Abstract: We propose a method to optimize the correlation among convolutional neural network (CNN) features that are used as inputs to quantum neural network (QNN) to enhance image classification accuracy.
By Minseo Seong, Youngwook Kim
arXiv:2506. 21324v3 Announce Type: replace-cross Abstract: Neuromorphic and quantum computing have recently emerged as promising paradigms for advancing artificial intelligence, each offering complementary strengths.
By Jiechen Chen, Bipin Rajendran, Osvaldo Simeone
We propose a method to optimize the correlation among convolutional neural network (CNN) features that are used as inputs to quantum neural network (QNN) to enhance image classification accuracy. Unlike prior approaches that employ orthogonal decomposition as preprocessing, we intentionally introduce correlated features that are more physically compatible with QNN.
The paper introduces Q‑MET, a quantum‑assisted framework that uses a hybrid quantum‑classical neural network to generate parameters for Wi‑Fi‑based human activity recognition models, drastically cutting the number of trainable parameters. By combining this approach with structured pruning during training, Q‑MET achieves 90–95% fewer trainable parameters than traditional backpropagation while preserving or improving classification accuracy. The method also yields 75–85% model sparsity for lightweight inference with less than 2% accuracy loss, enabling deployment on resource‑constrained devices.
By To Truong An, Jie Zhang, Guolin Yin, Junqing Zhang, Yanjiao Li, Trung Q. Duong, Simon L. Cotton
arXiv:2608. 01194v1 Announce Type: cross Abstract: Artificial intelligence has been transformed by deep neural networks, yet the search for new learning architectures continues.
By L\'eo Monbroussou, Maniraman Periyasamy, Viacheslav Kuzmin, Pavel Sekatski, Viktoria Patapovich, Asel Sagingalieva, Alexey Melnikov