QAOA for random k-SAT derives efficacy from an adiabatic manifold that supports rigorous performance guarantees at depth Θ(n²) and sublinear parameter optimization via SAMP at depth O(n).
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Human social organization is shaped by computational constraints on collective judgment and action, creating an opportunity for a new field combining theoretical computer science with social science.
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Mechanism of Efficacy in QAOA for Random k-SAT: From Adiabatic Manifold to Sublinear Parameter Optimization
QAOA for random k-SAT derives efficacy from an adiabatic manifold that supports rigorous performance guarantees at depth Θ(n²) and sublinear parameter optimization via SAMP at depth O(n).
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Computational foundations of the human world
Human social organization is shaped by computational constraints on collective judgment and action, creating an opportunity for a new field combining theoretical computer science with social science.