An LLM-driven workflow proposes and validates quantum control protocols by simulation, beating literature baselines on Rydberg MIS, XXZ chains, and random Ising models, and transferring learned counterdiabatic coefficients to larger systems via a graph neural network.
Albarrán-Arriagada and J
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LLM-Driven Cross-Paradigm Design for Quantum Optimal Control
An LLM-driven workflow proposes and validates quantum control protocols by simulation, beating literature baselines on Rydberg MIS, XXZ chains, and random Ising models, and transferring learned counterdiabatic coefficients to larger systems via a graph neural network.