Presents the first iterative spectral algorithm for near-optimal solutions to random quadratic optimization over the hypercube, resolving Subag's conjecture via potential Hessian ascent and SDE approximation.
Local algorithms and the failure of log-depth quantum advantage on sparse random CSPs
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Absence of quantum advantage for log-depth QAOA on the binary paint shop problem implies a classical mean-field algorithm achieving a paint-swap ratio of approximately 0.2799, outperforming known heuristics and quantum methods.
The paper benchmarks approximation techniques and transfer learning for setting QAOA angles at utility scale and extracts operational guidance from hardware-validated results.
The paper identifies four key hurdles in the transition from NISQ to FASQ quantum computers and argues that targeting them will accelerate progress toward useful quantum advantage.
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