arXiv (All)
open access
Variational Quantum Attention for Molecular Graph Learning
Molecular property prediction is central to computational drug discovery, where graph neural networks learn to weight neighboring atomic environments during message passing. Yet it remains unclear how variational quantum circuits alter learned attention behavior in molecular graphs. We introduce an
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
- Memory processing in neurodevelopmental disorders: linking behavioral, circuits, and molecular scales.#975696
- QLAM: A Quantum Long-Attention Memory Approach to Long-Sequence Token Modeling#180640
- Decentralized graph attention multi-agent reinforcement learning for adaptive urban traffic routing.#265452
- A Controlled Study of Physics-Informed Auxiliary Supervision and Scalar Triplet Attention in Equivariant Molecular Force Fields.#319126
- Forcing a Molecule to Switch: Quantifying Mechanical Control at the Atomic Scale#446503
Record · ID 1200239
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