QADR decomposes n-qubit VQCs into local sub-circuits to reduce memory from O(2^n) to O(n * 2^{2d+1}) and mitigate barren plateaus, scaling to 2000 features on MNIST and wind turbine diagnostics while matching classical models.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Systematic D-Wave experiments across Max-Cut, Number Partitioning, and sparse clustering show reverse annealing yields larger efficiency gains than longer forward anneals, with benefits growing for larger, more complex instances.
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Quantum Algorithm for Distributed Reduction of Entanglements (QADR): A Trainable and Simulation-Efficient QML Framework
QADR decomposes n-qubit VQCs into local sub-circuits to reduce memory from O(2^n) to O(n * 2^{2d+1}) and mitigate barren plateaus, scaling to 2000 features on MNIST and wind turbine diagnostics while matching classical models.
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Extending the computational reach of Quantum Annealing using Reverse Annealing
Systematic D-Wave experiments across Max-Cut, Number Partitioning, and sparse clustering show reverse annealing yields larger efficiency gains than longer forward anneals, with benefits growing for larger, more complex instances.