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
arXiv:2604. 06135v2 Announce Type: replace-cross Abstract: Efficient data loading remains a bottleneck for near-term quantum machine learning.
By Basil Kyriacou, Viktoria Patapovich, Maniraman Periyasamy, Alexey Melnikov
arXiv:2606. 26312v1 Announce Type: cross Abstract: Autoencoders transformed classical machine learning by solving the curse of dimensionality, enabling principled weight initialization and learning compact, structured representations.
By Aldo Lamarre, Dominik \v{S}afr\'anek
Large-scale Vision-Language Models have demonstrated impressive transfer learning capabilities across a wide range of tasks. For few-shot classification, we observe that VLMs exhibit a notable ability to filter candidate categories and thus achieve high Top-K accuracy.
Hybrid Variational Quantum-Classical Framework with Adaptive Weighting and Efficiency Assessment introduces Sim‑HVQC, a hybrid deep quantum neural network that integrates an adaptive, parameter‑free SimAM weighting module with classical feature extraction to retain class‑discriminative information before encoding into a Variational Quantum Circuit. Unlike prior work limited to binary classification, this framework is trained and evaluated on multiple multi‑class datasets such as MNIST, KMNIST, Fashion‑MNIST, and EMNIST. The study highlights reproducibility, parameter efficiency, and interpretability through multi‑seed evaluation, parameter analysis, and latent/quantum feature inspection, with source code publicly available on GitHub.
By Dilli Hang Rai
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
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