arXiv:2607. 19327v1 Announce Type: new Abstract: Associative emotional learning enables organisms to adaptively link pleasant or unpleasant outcomes to the presence of predictive stimuli.
By Seowung Leem, Andreas Keil, Mingzhou Ding, Ruogu Fang
arXiv:2608. 15601v1 Announce Type: new Abstract: Compositional Concept Generalization (CoCoGen), the ability to systematically recombine learned primitives in novel contexts, is a key challenge for multimodal learning.
By Mina Abbaszadeh, Matilda Karabina Moore, Mehrnoosh Sadrzadeh, Martha Lewis
arXiv:2502. 09928v2 Announce Type: replace-cross Abstract: Originating in quantum physics, tensor networks (TNs) have been widely adopted as exponential machines and parametric decomposers for recognition tasks.
By Chang Nie
arXiv:2410. 17397v2 Announce Type: replace-cross Abstract: We introduce a framework for seamlessly integrating quantum computing into pretrained large language models (LLMs).
By Borja Aizpurua, Fernando Loren, Saeed S. Jahromi, Sukhbinder Singh, Roman Orus
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
The paper evaluates hybrid quantum‑classical machine learning for predicting reduced‑order spatiotemporal brain deformation fields. Using Proper Orthogonal Decomposition to compress high‑dimensional displacement data, the authors compare static temporal‑to‑latent regression and autoregressive latent forecasting models. Classical neural networks outperform all quantum variants, though enhanced quantum circuits improve over minimal ones, indicating that classical architectures still hold a clear advantage in fidelity and stability for this task.
By Tao Liu, Ge He, Dongyu Liang, Wujie Wen