arXiv:2607. 01336v1 Announce Type: cross Abstract: Neural Quantum States (NQS) are a remarkably expressive class of variational ans\"atze for quantum many-body wavefunctions, yet little is understood about their internal mechanisms: trained on variational objectives alone, how do NQS accurately capture physical observables that they have never been explicitly optimized for?
By Zihao Qi, Christopher Earls
Most multimodal learning methods improve how heterogeneous representations are aligned and fused, while post-fusion enhancement remains less explored. We propose Parallel Quantum Feature Augmentation (PQFA), a hybrid quantum-classical framework that applies multiple shallow variational quantum circuits to fused multimodal features.
arXiv:2607. 02363v1 Announce Type: cross Abstract: Quantum Fast-Weight Programmers (QFWPs) store temporal information in dynamically programmed variational-circuit parameters rather than in nonlinear recurrent hidden states, offering a practical route to quantum sequence modeling.
By Kuo-Chung Peng, Jiun-Cheng Jiang, Chun-Hua Lin, Yifeng Peng, Junghoon Justin Park, Huan-Hsin Tseng, Hsin-Yi Lin, Kuan-Cheng Chen, Chen-Yu Liu, Shinjae Yoo, Samuel Yen-Chi Chen
The paper introduces Quantum-Inspired Nonlinear Adapters (QINA), compact modules that apply learnable trigonometric feature lifting followed by bounded nonlinear aggregation to pretrained vision models. QINA enables structured oscillatory basis functions with a norm-dependent Lipschitz bound, allowing spectral reshaping of representations without expanding the receptive field or significantly increasing parameters. Experiments on natural and medical imaging tasks show that QINA consistently outperforms identity baselines, fixed Fourier mappings, and parameter-matched generic adapters, demonstrating that geometry- and spectrum-aware adaptation is crucial for effective frozen-backbone transfer learning.
By Mostafa Mehdipour Ghazi
arXiv:2607. 21688v1 Announce Type: cross Abstract: Machine-learning surrogates of physical systems face a paradox: explainable models facing the challenge of expressivity to capture complex nonlinear flows, whereas expressive deep surrogates match high-fidelity simulations only through massive parameterisations that turn the learned dynamics into a black box.
By Xiao Xue, Maida Wang, Mingyang Gao, Minh Chung, Peter V. Coveney
arXiv:2504. 20823v3 Announce Type: replace Abstract: Accurate remaining useful life (RUL) estimation underpins safe operation and cost-effective maintenance of aerospace propulsion systems.
By Olga Tsurkan, Aleksandra Konstantinova, Arsenii Senokosov, Asel Sagingalieva, Alexey Melnikov