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
The paper identifies a representational limitation of amplitude encoding in quantum neural networks: normalization makes the encoding invariant to nonzero real rescaling of inputs, which restricts the hypothesis class regardless of the circuit architecture. The authors derive a condition for deterministic preprocessing that removes this ambiguity and propose a fixed nonlinear feature map that distinguishes all real inputs while keeping logarithmic qubit scaling. Experiments on three classification benchmarks and a synthetic dataset show that this new encoding improves accuracy by 21–44 % over both conventional amplitude and angle encodings and a classical neural network.
By Vincent Gurgul, David Prinz
arXiv:2410. 17397v2 Announce Type: replace-cross Abstract: We introduce a framework for seamlessly integrating quantum computing into pretrained large language models (LLMs).
By Borja Aizpurua, Fernando Loren, Saeed S. Jahromi, Sukhbinder Singh, Roman Orus
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: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
arXiv:2606. 00045v1 Announce Type: new Abstract: Classical continuous-space neural networks fundamentally struggle to lock into exact mathematical symmetries, such as modular arithmetic and non-commutative algebra.
By Sungyong Chung, Alireza Talebpour
arXiv:2606. 29966v1 Announce Type: cross Abstract: Quantum computing provides a powerful paradigm for representing and transforming high-dimensional information through superposition, entanglement, and measurement-induced nonlinear features.
By Xindian Ma, Xinyu Long, Yefei Zhang, Yanchen Liu, Xianghao Li, Yufu Wen, Yike Hu, Yuedong Zhu, Zeyang Ma, Wen Qin, Yikun Wang, Peng Yang, Monan Wang, Teng Yu
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
arXiv:2602. 06699v2 Announce Type: replace-cross Abstract: We propose a variational quantum implementation of self-attention (QSA)-the core operation in transformers and large language models-which predicts future elements of a sequence by forming overlap-weighted combinations of past data.
By Alessio Pecilli, Matteo Rosati
arXiv:2608. 06846v1 Announce Type: cross Abstract: We test whether a parameterized quantum circuit (PQC) improves a hybrid quantum-classical model's performance on classical datasets, using an interface-matched classical map as the control while holding all other components fixed.
By Hao-Yuan Chen
arXiv:2504. 20823v3 Announce Type: replace Abstract: Accurate remaining useful life (RUL) estimation underpins safe operation and cost-effective maintenance of aerospace propulsion systems.
By Olga Tsurkan, Aleksandra Konstantinova, Arsenii Senokosov, Asel Sagingalieva, Alexey Melnikov
The paper introduces a compact, NISQ‑compatible quantum transformer for synthetic QNLP sequence modelling. It replaces classical attention and feed‑forward layers with variational quantum encoder blocks, connector circuits, decoder blocks, and a two‑qubit measurement readout, while preserving the autoregressive next‑token interface. The authors evaluate several variants on deterministic and lexicographic grammar‑generation tasks, finding that the quantum models can learn nontrivial grammar structure but are less accurate and stable than a compact classical transformer baseline.
By Julian Hager, Michael K\"olle, Gerhard Stenzel, Tobias Rohe, Jonas Stein, Claudia Linnhoff-Popien