A DQN-guided controller selectively invokes quantum sampling within ALNS repair for constrained VRP, finding quantum repair admissible in ~16% of states but beneficial in 29/36 matched-budget settings.
Quantum-enhanced markov chain monte carlo for combinatorial optimization
3 Pith papers cite this work. Polarity classification is still indexing.
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NDAR, a heuristic that turns device noise into a resource, is generalized to integer-domain optimization; qudit-native encodings are argued to be the best fit because their all-zeros attractor is always feasible and their gauge freedom is maximal.
An iterative nonvariational quantum algorithm using warm-start states and classically computed imaginary time evolution circuits achieves median solutions within 95% of optimal for MaxCut on small 3-regular graphs using only 100 shots, outperforming random and basic classical searches.
citing papers explorer
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RL-Guided Quantum-ALNS for Constrained VRP
A DQN-guided controller selectively invokes quantum sampling within ALNS repair for constrained VRP, finding quantum repair admissible in ~16% of states but beneficial in 29/36 matched-budget settings.
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Noise-Directed Adaptive Remapping for Integer Optimization: from qubits to (encoded) qudits
NDAR, a heuristic that turns device noise into a resource, is generalized to integer-domain optimization; qudit-native encodings are argued to be the best fit because their all-zeros attractor is always feasible and their gauge freedom is maximal.
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Iterative warm-start optimization with quantum imaginary time evolution
An iterative nonvariational quantum algorithm using warm-start states and classically computed imaginary time evolution circuits achieves median solutions within 95% of optimal for MaxCut on small 3-regular graphs using only 100 shots, outperforming random and basic classical searches.