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.
Challenges and opportunities in quantum optimiza- tion
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VarQEC uses a distinguishability loss as a machine-learning objective to variationally discover resource-efficient encoding circuits optimized for given noise models.
Graph contraction reduces TSP instances to smaller sub-problems solvable by quantum annealers, shown via Path Integral Monte Carlo simulation and D-Wave hardware.
A review describing the Decoded Quantum Interferometry algorithm for quantum speedups in max-LINSAT optimization, with claimed superpolynomial advantage in the OPI problem.
citing papers explorer
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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.
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Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correction
VarQEC uses a distinguishability loss as a machine-learning objective to variationally discover resource-efficient encoding circuits optimized for given noise models.
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A Hybrid Classical-Quantum Annealing Algorithm for the TSP
Graph contraction reduces TSP instances to smaller sub-problems solvable by quantum annealers, shown via Path Integral Monte Carlo simulation and D-Wave hardware.
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Quantum Decoding Algorithms: Quantum Speedups in Optimization
A review describing the Decoded Quantum Interferometry algorithm for quantum speedups in max-LINSAT optimization, with claimed superpolynomial advantage in the OPI problem.