ArticleScientific reports2024
Applying quantum approximate optimization to the heterogeneous vehicle routing problem.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- The Quantum Optimization Benchmarking Library.Nature computational science · 2026Article
- The capacitated arc routing problem for on-road anti-dust operations.Scientific reports · 2026Article
- Multi-Objective Routing Optimization for 6G Communication Networks Using a Quantum Approximate Optimization Algorithm.Sensors (Basel, Switzerland) · 2022Article
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5 authors.
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Abstract
Quantum computing offers new heuristics for combinatorial problems. With small- and intermediate-scale quantum devices becoming available, it is possible to implement and test these heuristics on small-size problems. A candidate for such combinatorial problems is the heterogeneous vehicle routing problem (HVRP): the problem of finding the optimal set of routes, given a heterogeneous fleet of vehicles with varying loading capacities, to deliver goods to a given set of customers. In this work, we investigate the potential use of a quantum computer to find approximate solutions to the HVRP using the quantum approximate optimization algorithm (QAOA). For this purpose we formulate a mapping of the HVRP to an Ising Hamiltonian and simulate the algorithm on problem instances of up to 21 qubits. We show that the number of qubits needed for this mapping scales quadratically with the number of customers. We compare the performance of different classical optimizers in the QAOA for varying problem size of the HVRP, finding a trade-off between optimizer performance and runtime.
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