A MISDP formulation approximates QAOA cost matrices for native hardware embedding without SWAPs, backed by NP-completeness proof and Lovasz-number bounds, yielding competitive performance on cardinality-constrained quadratic optimization.
Approximate graph coloring by semidefinite programming.Journal of the ACM (JACM), 45(2):246–265
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 2verdicts
UNVERDICTED 2representative citing papers
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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A SWAP-free Framework for QAOA
A MISDP formulation approximates QAOA cost matrices for native hardware embedding without SWAPs, backed by NP-completeness proof and Lovasz-number bounds, yielding competitive performance on cardinality-constrained quadratic optimization.
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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.