arXiv:2412. 09486v2 Announce Type: replace-cross Abstract: The literature reflects a mutually beneficial relationship between machine learning and quantum computing, where progress in one field frequently drives improvements in the other.
By Leandro C. Souza, Bruno C. Guingo, Gilson Giraldi, Renato Portugal
arXiv:2605. 22097v2 Announce Type: replace-cross Abstract: Photonic quantum computing is a promising platform for scalable quantum machine learning, but designing effective hybrid architectures remains challenging under hardware and optimization constraints.
By Farah Elnakhal, Alberto Marchisio, Nouhaila Innan, Gabriel Falcao, Muhammad Shafique
arXiv:2606. 03517v1 Announce Type: cross Abstract: Training quantum neural networks (QNNs) on quantum hardware is currently bottlenecked by the cost of gradient estimation: standard parameter-shift methods require a number of circuit evaluations that grows quadratically with the number of trainable parameters, making hardware-based optimisation impractical beyond small system sizes.
By Natansh Mathur, Panagiotis Kl. Barkoutsos, Masako Yamada, Martin Roetteler, Iordanis Kerenidis
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:2607. 27262v1 Announce Type: cross Abstract: Variational quantum algorithms often encounter barren plateaus, where cost gradients decay rapidly with increasing circuit depth, undermining the trainability of parameterized quantum circuits.
By Riza Alaudin Syah, Irwan Alnarus Kautsar, Haza Nuzly Bin Abdull Hamed
arXiv:2606. 28655v1 Announce Type: cross Abstract: Parameterized quantum circuits (PQCs) provide a flexible substrate for hybrid quantum machine learning (QML), but their practical value on Noisy Intermediate-Scale Quantum (NISQ) devices remains an empirical question, especially because training depth and scale can introduce optimization challenges such as barren plateaus.
By Aspen Erlandsson Brisebois, Luis Pablo Gonzalez Dominguez, Shivansi Prajapati, Zahed Khatooni, Heather L. Wilson, Connor Burbridge, Brook Byrns, Sureesh Tikoo, Christophe Pere, Steven Rayan, Gordon Broderick