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.
Trainability barriers in low-depth qaoa landscapes
2 Pith papers cite this work. Polarity classification is still indexing.
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Circuit replication reduces result variability in QAOA but also lowers inference strength, with effects differing between small and large graphs under real-world noise.
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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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Profiling the Effective Limits of Error Mitigation via Circuit Replication
Circuit replication reduces result variability in QAOA but also lowers inference strength, with effects differing between small and large graphs under real-world noise.