Fragment classification is efficiently learnable by quantum neural networks under suitable conditions but resists known classical dequantization techniques.
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Balanced k-way hypergraph partitioning is cast as QUBO and higher-order binary problems for quantum optimization, with small-instance tests confirming effectiveness for the all-or-nothing cut on 3-uniform hypergraphs.
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Fragmentation is Efficiently Learnable by Quantum Neural Networks
Fragment classification is efficiently learnable by quantum neural networks under suitable conditions but resists known classical dequantization techniques.
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Quantum Hypergraph Partitioning
Balanced k-way hypergraph partitioning is cast as QUBO and higher-order binary problems for quantum optimization, with small-instance tests confirming effectiveness for the all-or-nothing cut on 3-uniform hypergraphs.