arXiv Machine Learning September 17, 2026 By Julian Hager, Michael K\"olle, Gerhard Stenzel, Tobias Rohe, Jonas Stein, Claudia Linnhoff-Popien

Variational Quantum Transformer Architecture for Synthetic Language Generation

Read the original on arXiv Machine Learning →

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.

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