The work proves that approximating correlation clustering to additive εn² error requires Ω(n/ε²) adjacency-matrix queries, with stronger bounds under memory constraints in random and general query models.
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A polylog-sized quantum computer achieves exponential advantage over classical machines in classification and dimension reduction of massive classical data using quantum oracle sketching combined with classical shadows.
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Query Lower Bounds for Correlation Clustering under Memory Constraints
The work proves that approximating correlation clustering to additive εn² error requires Ω(n/ε²) adjacency-matrix queries, with stronger bounds under memory constraints in random and general query models.
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Exponential quantum advantage in processing massive classical data
A polylog-sized quantum computer achieves exponential advantage over classical machines in classification and dimension reduction of massive classical data using quantum oracle sketching combined with classical shadows.