Hugging Face Trending Papers
Variational Quantum Transformer Architecture for Synthetic Language Generation
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arXiv Machine Learning
Sep 17
Variational Quantum Transformer Architecture for Synthetic Language Generation
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.
Hugging Face Trending Papers
Jul 14
When Close Enough Is Not Enough: Autoregressive Drift in Quantum Circuit Synthesis
Quantum circuit optimization for fault-tolerant computing requires exact functional equivalence while minimizing expensive non-Clifford resources such as T gates. We study this problem using a compact 44.
arXiv Computation and Language
Sep 22
Circuit Hypernetworks for Quantum-Augmented Diffusion Language Models
arXiv:2609.24657v1 Announce Type: cross Abstract: Language models can be adapted by changing the computations applied to individual tokens. Quantum circuits offer one such approach, but evaluating wi...
arXiv Machine Learning
Jun 25
Recursive QLSTM with Dynamic Variational Quantum Circuit Adaptation
arXiv:2606. 24932v1 Announce Type: cross Abstract: Recent advances in quantum computing and machine learning have motivated the development of quantum models for sequential data processing.
arXiv Machine Learning
Sep 14
QTrans: A Quantum Transformer for Sentiment Classification
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.
arXiv AI
Jul 15
When Close Enough Is Not Enough: Autoregressive Drift in Quantum Circuit Synthesis
arXiv:2607. 12780v1 Announce Type: cross Abstract: Quantum circuit optimization for fault-tolerant computing requires exact functional equivalence while minimizing expensive non-Clifford resources such as T gates.