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.
Algorithms and barriers in the symmetric binary perceptron model
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A nested local-entropy-bias ensemble reveals a delocalized cluster of connected solutions in the symmetric binary perceptron, with a predicted stability threshold where solution paths shatter.
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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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Finding the right path: statistical mechanics of connected solutions in constraint satisfaction problems
A nested local-entropy-bias ensemble reveals a delocalized cluster of connected solutions in the symmetric binary perceptron, with a predicted stability threshold where solution paths shatter.