Abstract claims zero-shot 5-to-10-city EQC transfer beats target-size training only in exact simulation, degrading by 31.3% under sampling noise and 45.3% on hardware; the supplied body omits these experiments.
Generalization in deep rl for tsp problems via equivariance and local search.SN Computer Science, 5(4):369, 2024
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Diagnosing Simulation and Hardware Barriers to Cross-Size Transfer in Equivariant Quantum Reinforcement Learning
Abstract claims zero-shot 5-to-10-city EQC transfer beats target-size training only in exact simulation, degrading by 31.3% under sampling noise and 45.3% on hardware; the supplied body omits these experiments.