Hybrid Quantum Annealing Approach for High-Dimensional and Multi-Criteria Constrained Quadratic Optimization in Arctic Ship Routing

TL;DR

This paper introduces a hybrid quantum annealing method for solving complex, high-dimensional Arctic ship routing problems with multiple criteria, demonstrating significant speedups and improved route quality over classical methods.

Contribution

It formulates a multi-criteria Arctic route optimization problem as a Constrained Quadratic Model and applies a hybrid quantum-classical solver, showing advantages over classical solvers in scalability and solution quality.

Findings

Achieves 10 to 100 times faster convergence than classical solvers.

Maintains stable runtimes as quadratic density increases.

Improves route smoothness and reduces total length.

Abstract

The opening of Arctic sea routes presents unprecedented opportunities for global trade but poses significant operational and computational challenges due to the dynamic nature of sea ice conditions. This study formulates a multi criteria Arctic route optimization problem that integrates Copernicus Marine Environment Monitoring Service (CMEMS) variables into a Constrained Quadratic Model (CQM) and solves it using D Wave's hybrid quantum classical solver. We benchmark the feasibility and scalability of this approach against classical Mixed Integer Quadratic Programming (MIQP) solvers such as Gurobi and CPLEX. Results show that the CQM formulation achieves feasible solutions with stable runtimes as quadratic density increases, demonstrating 10 to 100 times faster convergence and reduced computational time compared with classical solvers, while also improving route smoothness by approximately 10 percent and reducing total length by approximately 1 percent. This reflects the effectiveness of the hybrid quantum annealing approach for Arctic routing problems.