QTrans is a quantum transformer designed for small‑scale binary sentiment classification. It constructs query, key, and value features using parameterized quantum circuits and derives attention coefficients from Gaussian distances between quantum measurements. The model incorporates a quantum feed‑forward network, residual connections, and layer normalization, achieving higher accuracies on MR, CR, and MPQA datasets compared to classical baselines.
By Ren-Xin Zhao, Xinjie Huang, Yahong Liu, Maoyu Ye, Jinjing Shi, Shi Wang, Yaonan Wang
Mu-DisCoCat is a multimodal variational quantum learning framework that maps Compositional Distributional Semantics (DisCoCat) onto Variational Quantum Circuits (VQCs) to achieve compositional concept generalization (CoCoGen). The pipeline first learns stable object representations from single-object image-text pairs, then fixes these to learn relations in multi-object scenarios. In classical simulations it outperformed a CLIP baseline on out‑of‑distribution relational accuracy, and on noisy quantum emulators and real IBM and IQM hardware it maintained strong fidelity correlations, reliably distinguishing unseen similar and dissimilar pairs.
By Mina Abbaszadeh, Matilda Karabina Moore, Raem Haq, Martha Lewis, Mehrnoosh Sadrzadeh
arXiv:2609.25044v1 Announce Type: cross
Abstract: This paper presents a quantum semantic communication (QSemCom) framework combining quantum machine learning (QML) and semantic communication (SemCom)...
By Melek Krichen, Nikhitha Nunavath, Riccardo Bassoli, Soumaya Cherkaoui
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
Quantum machine learning has recently emerged as a promising paradigm that leverages the expressive power of quantum circuits to address complex learning tasks. In this work, we investigate the applicability of hybrid quantum-classical neural networks to sentiment analysis, a central problem in natural language processing.
arXiv:2607. 01943v1 Announce Type: new Abstract: Quantum machine learning has recently emerged as a promising paradigm that leverages the expressive power of quantum circuits to address complex learning tasks.
By Giacomo Cappiello, Filippo Caruso, Xing Liang, Dimitrios Makris