1 research outputs found
Performance of Commercial Quantum Annealing Solvers for the Capacitated Vehicle Routing Problem
Quantum annealing (QA) is a heuristic search algorithm that can run on
Adiabatic Quantum Computation (AQC) processors to solve combinatorial
optimization problems. Although theoretical studies and simulations on classic
hardware have shown encouraging results, these analyses often assume that the
computation occurs in adiabatically closed systems without environmental
interference. This is not a realistic assumption for real systems; therefore,
without extensive empirical measurements on real quantum platforms,
theory-based predictions, simulations on classical hardware or limited tests do
not accurately assess the current commercial capabilities. This study has
assessed the quality of the solution provided by a commercial quantum annealing
platform compared to known solutions for the Capacitated Vehicle Routing
Problem (CVRP). The study has conducted extensive analysis over more than 30
hours of access to QA commercial platforms to investigate how the size of the
problem and its complexity impact the solution accuracy and the time used to
find a solution. Our results have found that the absolute error is between 0.12
and 0.55, and the quantum processor unit (QPU) time is between 30 and 46 micro
seconds. Our results show that as the constraint density increases, the quality
of the solution degrades. Therefore, more than the problem size, the model
complexity plays a critical role, and practical applications should select
formulations that minimize the constraint density