arXiv (All)
open access
Inductive Graph Representation Learning with Quantum Graph Neural Networks
Graph Neural Networks (QGNNs) offer a promising approach to combining quantum computing with graph-structured data processing. While classical Graph Neural Networks (GNNs) are scalable and robust, existing QGNNs often lack flexibility due to graph-specific quantum circuit designs, limiting their app
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
- Sampled-Based Guided Quantum Walk: Non-variational quantum algorithm for combinatorial optimization#1156210
- Quantum minds: Merging quantum computing with next-gen AI#108888
- Quantum minds: Merging quantum computing with next-gen AI#74393
- Parallel Data Processing in Quantum Machine Learning#635629
- Parallel Data Processing in Quantum Machine Learning#633280
Record · ID 1036546
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