SAFE ma-QAOA achieves 64.3% fewer active parameters and 94.5% lower estimated QPU workload via surrogate pre-training and parameter distillation on Sherrington-Kirkpatrick, 2D spin glass, and Max-Cut instances.
For fixed control parameters the quantum approximate optimization algorithm’s objective function value concentrates for typical instances,
2 Pith papers cite this work. Polarity classification is still indexing.
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A generative model learns patterns from adaptive QAOA circuits to generate high-quality shallow quantum circuits for Max-E3-SAT that scale better than variational baselines.
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SAFE ma-QAOA: Surrogate-Assisted and Fine-Tuning Enhanced Multi-Angle QAOA with Parameter Distillation
SAFE ma-QAOA achieves 64.3% fewer active parameters and 94.5% lower estimated QPU workload via surrogate pre-training and parameter distillation on Sherrington-Kirkpatrick, 2D spin glass, and Max-Cut instances.
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Q3SAT-GPT: A Generative Model for Discovering Quantum Circuits for the 3-SAT Problem
A generative model learns patterns from adaptive QAOA circuits to generate high-quality shallow quantum circuits for Max-E3-SAT that scale better than variational baselines.