arXiv (All)
open access
Circuit Hypernetworks for Quantum-Augmented Diffusion Language Models
Language models can be adapted by changing the computations applied to individual tokens. Quantum circuits offer one such approach, but evaluating wider circuits inside a large model can be computationally demanding. Here we introduce HyperQ, which adds token-conditioned quantum residual branches to
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- PAS-QFL: Personalized Ansatz Selection for Quantum Federated Learning under Client Data Heterogeneity#612431
- PAS-QFL: Personalized Ansatz Selection for Quantum Federated Learning under Client Data Heterogeneity#610954
- PAS-QFL: Personalized Ansatz Selection for Quantum Federated Learning under Client Data Heterogeneity#459634
- Quantum Coordination Advantages in AI State-Tracking Tasks: Semantic Compilation and Latent Memory#444666
- Distributing Quantum Computations, Shot-Wise#274736
Record · ID 1051064
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive