arXiv (All)
open access
Quantum Approximate Multi-Objective Optimization in Routing Problems
Multi-objective optimization (MOO) problems are common in logistics, where routing decisions must balance conflicting objectives such as travel distance, delivery time, and operational risk. A recently proposed Quantum Approximate Optimization Algorithm (QAOA) parameter-transfer strategy solves mult
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
- Solving Multiple Discretization Portfolio Optimization Problem with Quantum-Classical Hybrid Algorithms#299215
- Performance guarantees of light-cone variational quantum algorithms for the maximum cut problem#793426
- Performance guarantees of light-cone variational quantum algorithms for the maximum cut problem#789422
- Multi-objective optimisation applied to industrial energy problems#456707
- Traveling Salesman Problem with a preprocessing method for classical and quantum optimization#925966
Record · ID 1154791
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