QAOA produces distributional solutions for hypergraph partitioning that outperform classical SDP approximations on fairness-style objectives like greatest expected imbalance.
Expected maximin fairness in max-cut and other combinatorial optimization problems
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QAOA ansatz with finite layers can capture any bitstring distribution and solves the Fair Cut Cover problem with provable and empirical advantages over classical approximations on certain graphs.
citing papers explorer
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Quantum Hypergraph Partitioning
QAOA produces distributional solutions for hypergraph partitioning that outperform classical SDP approximations on fairness-style objectives like greatest expected imbalance.
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Learning Cut Distributions with Quantum Optimization
QAOA ansatz with finite layers can capture any bitstring distribution and solves the Fair Cut Cover problem with provable and empirical advantages over classical approximations on certain graphs.