Adding loop composition to branching quantum walk models produces a variable-time quantum search algorithm whose complexity matches the best known results.
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AHGCDD distills large hypergraphs into informative synthetic versions via anchor-guided joint optimization and dual-level discrimination, achieving better effectiveness and efficiency than prior decoupled HGC approaches.
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Loop Composition in Quantum Algorithms
Adding loop composition to branching quantum walk models produces a variable-time quantum search algorithm whose complexity matches the best known results.
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Anchor-guided Hypergraph Condensation with Dual-level Discrimination
AHGCDD distills large hypergraphs into informative synthetic versions via anchor-guided joint optimization and dual-level discrimination, achieving better effectiveness and efficiency than prior decoupled HGC approaches.