arXiv (All)
open access
QFOR: A Fidelity-aware Orchestrator for Quantum Computing Environments using Deep Reinforcement Learning
Quantum cloud computing enables remote access to quantum processors, yet the heterogeneity and noise of available quantum hardware create significant challenges for efficient resource orchestration. These issues complicate the optimization of quantum task allocation and scheduling, as existing heuri
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
- The Convergence of Quantum Computing and Machine Learning: A Path to Accelerating AI Solutions In#72262
- DRLQ: A Deep Reinforcement Learning-based Task Placement for Quantum Cloud Computing#1062746
- Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuits#986247
- Quantum computing and Artificial Intelligence: Toward a new computational paradigm#112930
- QCOEM: Quantum Cloud Orchestration with Evolutionary Multi-Objective Optimization#411012
Record · ID 1062784
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