arXiv (All)
open access
Quantum-Inspired Genetic Optimization for Patient Scheduling in Radiation Oncology
Among the genetic algorithms generally used for optimization problems in the recent decades, quantum-inspired variants are known for fast and high-fitness convergence and small resource requirement. Here the application to the patient scheduling problem in proton therapy is reported. Quantum chromos
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
- Classical-Quantum Algorithm for Solving Stochastic Programs#631401
- SUTURE: Syndrome-Guided Repair for Segmented Feasibility-Preserving VQAs on Noisy Hardware#1083427
- Hybrid quantum-classical algorithms for high performance computing#357463
- FT-Weave: Real-Time Compilation Framework for Reconfigurable Fault-Tolerant Quantum Architectures#1034386
- Integrating AI, Machine Learning, and Quantum Computing for Advanced Diagnostic and Therapeutic Strategies in Modern Healthcare#95425
Record · ID 1342325
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