The authors cast rolling stock planning as maximum-weight independent set on a cycle graph, then apply a divide-and-conquer hybrid that solves subgraphs with QAOA (simulated and on IQM Emerald) and show larger subgraphs yield better solutions than smaller ones or pure classical heuristics.
Expectation values from the single-layer quantum approximate optimization algorithm on ising problems,
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Rolling Stock Planning Using the Quantum Approximate Optimization Algorithm
The authors cast rolling stock planning as maximum-weight independent set on a cycle graph, then apply a divide-and-conquer hybrid that solves subgraphs with QAOA (simulated and on IQM Emerald) and show larger subgraphs yield better solutions than smaller ones or pure classical heuristics.