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Variational Quantum Transformer for Synthetic Language Generation

Variational Quantum Transformer Architecture for Synthetic Language Generation

arXiv cs.LG@Julian Hager et al. (6)
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Why we picked it

This is a technical paper on a new architecture for quantum machine learning, which is relevant for researchers in the field.

A new architecture combines a classical transformer with variational quantum encoder blocks to model synthetic language. It was evaluated on grammar-generation tasks against a classical baseline, showing the quantum models learned some structure but were less accurate and stable.

Original · arXiv cs.LG

Variational Quantum Transformer Architecture for Synthetic Language Generation

We propose a compact NISQ-compatible quantum transformer architecture for synthetic QNLP sequence modelling. The model preserves the autoregressive next-token interface of a classical transformer, but replaces attention and feed-forward sublayers with variational quantum encoder blocks, connector circuits, decoder blocks and a direct two-qubit measurement readout. Token contexts are angle-encoded into small quantum registers, processed by parallel variational heads and encoder integration circuits and conditioned through decoder ancillae to produce a distribution over a four-token vocabulary. We evaluate several architecture variants on deterministic and lexicographic grammar-generation tasks against a compact classical transformer baseline. The quantum models are trainable end-to-end and learn nontrivial grammar structure, including perfect deterministic generation in individual runs and high lexicographic validity in the strongest variant. The classical baseline remains more accurate and stable and the quantum models are sensitive to initialization. The contribution is therefore not a claim of quantum advantage, but a concrete architecture and evaluation of transformer-inspired QNLP sequence modelling under near-term quantum constraints.

Industry Coordinates & Source Verification

This report is categorized under AITOP's "Papers" desk. Key entities and technical topics under continuous tracking include quantum transformer, synthetic language generation, QNLP, quantum machine learning.

Source attribution: curated from arXiv cs.LG (@Julian Hager, Michael Kölle, Gerhard Stenzel, Tobias Rohe, Jonas Stein, Claudia Linnhoff-Popien).

#quantum transformer#synthetic language generation#QNLP#quantum machine learning

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