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
arXiv:2607. 21647v1 Announce Type: new Abstract: Quantum federated learning enables distributed clients to train quantum neural networks without sharing local data, making it promising for privacy-aware intelligent services.
By Shanika Iroshi Nanayakkara, Shiva Raj Pokhrel
The paper introduces Q‑MET, a quantum‑assisted framework that uses a hybrid quantum‑classical neural network to generate parameters for Wi‑Fi‑based human activity recognition models, drastically cutting the number of trainable parameters. By combining this approach with structured pruning during training, Q‑MET achieves 90–95% fewer trainable parameters than traditional backpropagation while preserving or improving classification accuracy. The method also yields 75–85% model sparsity for lightweight inference with less than 2% accuracy loss, enabling deployment on resource‑constrained devices.
By To Truong An, Jie Zhang, Guolin Yin, Junqing Zhang, Yanjiao Li, Trung Q. Duong, Simon L. Cotton
The paper introduces vFedProtoQNAS, a prototype‑guided personalized quantum neural architecture search method for virtual federated learning. It allows each client to independently design and train a device‑specific quantum neural network while avoiding parameter aggregation across structurally different models. Instead, clients share class‑wise latent prototypes, which are refined using global prototypes from the server to serve as federated semantic anchors, leading to a 3.70% accuracy improvement over FedAvg.
By Seok Bin Son, Samuel Yen-Chi Chen, Soohyun Park, Joongheon Kim
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. 09422v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning is well suited to problems with large parameter spaces and exploitable local structure, such as the tuning of electrostatically-defined quantum-dot arrays.
By Edwin De Nicolo, Rahul Marchand, Cornelius Carlsson, Pranav Vaidhyanathan, Natalia Ares