Quantum-Assisted Maritime Route Optimization System

This paper presented QAMROS — Quantum-Assisted Maritime Route Optimization System — a complete, modular, and production-ready Python application integrating quantum-inspired optimization with classical maritime logistics analytics. The system constructs a weighted global port network of 40 ports and 150+ route connections, generates candidate paths through DFS and k-shortest path algorithms, evaluates each route across seven quantitative metrics, and applies a QAOA-inspired variational quantum circuit to identify the globally optimal route. Experimental results demonstrate that the quantum optimizer correctly identifies minimum composite cost routes across all evaluated port pairs, achieving significant improvement over distance-only greedy baselines — particularly in scenarios where risk and fuel efficiency diverge from shortest-distance criteria. The Streamlit dashboard provides a professional, interactive interface suitable for demonstrating the system to academic and industry audiences. QAMROS makes three primary contributions: (1) a complete, documented quantum-classical hybrid framework for maritime route optimization implementable on standard academic hardware; (2) a realistic maritime dataset covering global shipping patterns, risk zones, and multi-hop port networks; and (3) an educational, beginner-friendly codebase demonstrating QAOA circuit design, variational quantum optimization, and multi-metric logistics analytics in an integrated production system.

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