arXiv:2606. 14822v1 Announce Type: cross Abstract: Recent advances in Machine Learning have transformed numerous industrial sectors, yet classical paradigms face fundamental limitations: rapidly growing data volumes, rising computational costs, significant energy consumption, and the physical scaling limits of conventional hardware architectures.
By L\'eo Monbroussou
arXiv:2607. 21409v1 Announce Type: cross Abstract: A central challenge in quantum machine learning is understanding the scaling behavior of parameterized quantum circuits (PQCs).
By Marie Kempkes, Elies Gil-Fuster, Carlos Bravo-Prieto, Aroosa Ijaz, Alissa Wilms, Jens Eisert, Evert van Nieuwenburg, Vedran Dunjko
arXiv:2509. 14026v2 Announce Type: replace-cross Abstract: Variational quantum circuits (VQCs) are central to quantum machine learning, while recent progress in Kolmogorov-Arnold networks (KANs) highlights the power of learnable activation functions.
By Jiun-Cheng Jiang, Morris Yu-Chao Huang, Tianlong Chen, Hsi-Sheng Goan
arXiv:2607. 01329v1 Announce Type: cross Abstract: The geometric and topological structure of quantum cost landscapes (QCLs) governs the optimization and thus the predictive power of variational quantum algorithms (VQAs).
By Felix J. Beckmann, Jo\~ao F. Bravo
The paper introduces a hybrid quantum–classical regression framework that uses a lightweight classical embedding as a learnable geometric preconditioner to improve the conditioning of a downstream variational quantum circuit. It further incorporates a curriculum optimization protocol that gradually increases circuit depth and switches from SPSA-based exploration to Adam-based fine‑tuning. Experiments on PDE‑informed and standard regression datasets show that this approach consistently outperforms pure QNN baselines, yielding more stable convergence and reduced structured errors, especially in data‑limited regimes.
By Qingyu Meng, Yangshuai Wang
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