Efficient learning algorithms for energy estimation imply that stable quantum algorithms cannot prepare low-energy states in systems exhibiting the quantum overlap gap property, as proven for a sparsified quantum p-spin model.
Circuit lower bounds for the p-spin optimization problem
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Online algorithms achieve multiplicative approximation r^{1/(r-1)} for maximum independent sets in dense r-uniform ER hypergraphs and (max γ_i)^{-1/(r-1)} for balanced sets in r-partite versions, with matching lower bounds.
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Quantum Glassiness From Efficient Learning
Efficient learning algorithms for energy estimation imply that stable quantum algorithms cannot prepare low-energy states in systems exhibiting the quantum overlap gap property, as proven for a sparsified quantum p-spin model.
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Algorithmic Phase Transition for Large Independent Sets in Dense Hypergraphs
Online algorithms achieve multiplicative approximation r^{1/(r-1)} for maximum independent sets in dense r-uniform ER hypergraphs and (max γ_i)^{-1/(r-1)} for balanced sets in r-partite versions, with matching lower bounds.