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
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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quant-ph 2years
2026 2representative citing papers
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.
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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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.