arXiv:2602. 01177v3 Announce Type: replace-cross Abstract: We develop an information-theoretic framework connecting stability, privacy, and generalization for quantum learning algorithms.
By Ayanava Dasgupta, Naqueeb Ahmad Warsi, Masahito Hayashi
arXiv:2609.25082v1 Announce Type: new
Abstract: Quantum machine learning (QML) is increasingly recognized as one of the most promising near-term applications of quantum computing, viewed as a next-fr...
By Carlos Cano, Daniel M. Jimenez-Gutierrez, Diego Sal, Georgios Kellaris, Joaquin del Rio, Oleksii Sliusarenko, Xabi Uribe-Etxebarria
The paper investigates whether a learner or tester with limited resources can improve performance by interacting with an untrusted, resource‑unconstrained party. It shows that for many scenarios, classical interaction offers no advantage, especially for memory‑constrained quantum algorithms. However, when quantum communication is permitted, interactive proof protocols enable memory‑constrained quantum verifiers to achieve significant gains through delegation.
By Matthias C. Caro, Jens Eisert, Marcel Hinsche, Marios Ioannou, Alexander Nietner, Ryan Sweke
arXiv:2606. 01291v1 Announce Type: cross Abstract: Training Variational Quantum Circuits (VQCs) under Noisy Intermediate-Scale Quantum (NISQ) constraints introduces severe computational limitations: classical statevector simulation memory scales exponentially ($\mathcal{O}(2^n)$), and global cost functions suffer from barren plateaus where gradient variance decays exponentially ($\mathcal{O}(1/2^n)$).
By Syed Farhan Ahmad, Gregory T. Byrd
arXiv:2608. 14995v1 Announce Type: cross Abstract: Quantum federated learning (QFL) lets multiple quantum clients collaboratively train quantum neural networks (QNNs) without sharing private local data.
By Jindi Wu, Qun Li
arXiv:2607. 02444v1 Announce Type: cross Abstract: We study stabilizer state testing and learning with limited coherent quantum memory.
By Srinivasan Arunachalam, Louis Schatzki