RL-Guided Quantum-ALNS for Constrained VRP

• URL: http://arxiv.org/abs/2607.07550v1
• Date: Wed, 08 Jul 2026 15:48:27 GMT
• Title: RL-Guided Quantum-ALNS for Constrained VRP
• Abstract summary: We embed shallow quantum samplers inside the repair phase of an Adaptive Large Neighbourhood Search (ALNS)<n>A Deep Q-Network controller decides whether each reduced repair subproblem should be handled by a classical repair or by a quantum sampler.<n>Across the tested instances, quantum repair is admissible in only about 16% of reduced repair states and is not superior on average.
• Score: 1.8262547855491453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study develops a hybrid quantum-classical framework for constrained vehicle routing problems, focusing on the pickup-and-delivery problem with time windows. Instead of casting the full routing problem as a stand-alone quantum optimization task, we embed shallow quantum samplers inside the repair phase of an Adaptive Large Neighbourhood Search (ALNS) heuristic. A Deep Q-Network controller decides whether each reduced repair subproblem should be handled by a classical repair heuristic or by a quantum sampler, using features that describe the local repair structure and predicted hardware reliability. IBM Heron experiments are used to calibrate an empirical noise-aware model for local quantum repair circuits. Across the tested instances, quantum repair is admissible in only about 16% of reduced repair states and is not superior on average. However, under selected matched repair budgets, quantum-enabled repair reduces the final gap relative to standard ALNS in 29 of 36 tested settings. These results suggest that near-term quantum sampling is most useful as a selective local repair mechanism rather than as a replacement for classical routing heuristics.

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