Circuit Hypernetworks for Quantum-Augmented Diffusion Language Models
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
The Flow has not summarised this story yet — read it at arXiv Computation and Language.
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.
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.
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.
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...
arXiv:2609.37134v1 Announce Type: cross Abstract: Frozen language models (LMs) are increasingly used as fixed feature extractors for downstream reranking, scoring, and preference modeling, raising a...
arXiv:2410. 17397v2 Announce Type: replace-cross Abstract: We introduce a framework for seamlessly integrating quantum computing into pretrained large language models (LLMs).