Classical feedback-based optimization matches or exceeds quantum performance in speed and scalability while quantum retains an edge in final solution quality on tested instances.
Extending sample persistence variable reduction for constrained combinatorial optimization problems
4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4representative citing papers
Introducing a segment-length parameter into Ising-machine-assisted large neighborhood search for VRP yields ~10% better solutions than the prior method by enabling finer subproblem-size control.
Increasing the penalty coefficient improves sampling fairness in over 70% of tested weighted graph bipartitioning instances on both simulation and D-Wave hardware, at the expense of lower ground-state probability.
Augmented Lagrangian formulation cuts time-to-epsilon by an order of magnitude versus penalty methods on Ising machines for quadratic knapsack while keeping penalty parameters small.
citing papers explorer
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Feedback-based quantum optimization and its classical counterpart: quantum advantage and the power of classical algorithms
Classical feedback-based optimization matches or exceeds quantum performance in speed and scalability while quantum retains an edge in final solution quality on tested instances.
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Ising-Machine-Assisted Large Neighborhood Search with Flexibly Tunable Subproblem Size
Introducing a segment-length parameter into Ising-machine-assisted large neighborhood search for VRP yields ~10% better solutions than the prior method by enabling finer subproblem-size control.
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Unfair Sampling of Quantum Annealing in Weighted Graph Bipartitioning Problems
Increasing the penalty coefficient improves sampling fairness in over 70% of tested weighted graph bipartitioning instances on both simulation and D-Wave hardware, at the expense of lower ground-state probability.
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Evaluating the solution performance of the augmented Lagrangian function on Ising machines
Augmented Lagrangian formulation cuts time-to-epsilon by an order of magnitude versus penalty methods on Ising machines for quadratic knapsack while keeping penalty parameters small.