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.
Conic programming to understand sums of squares of eigenvalues of graphs
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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.