Universal optimal tracking designs state-independent dynamical decoupling sequences that compensate residual errors from control imperfections while preserving refocusing.
Lindoy, Deep Lall, Sebastian E
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Introduces QCalEval benchmark showing best zero-shot VLM score of 72.3 on quantum calibration plots, with fine-tuning and in-context learning effects varying by model type.
A hybrid optimal-control-plus-contextual-RL framework learns low-dimensional residual pulse corrections that preserve high-fidelity controlled-phase gates on two qutrits under realistic static model mismatch.
citing papers explorer
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Dynamical Decoupling using Universal Optimal Tracking
Universal optimal tracking designs state-independent dynamical decoupling sequences that compensate residual errors from control imperfections while preserving refocusing.
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QCalEval: Benchmarking Vision-Language Models for Quantum Calibration Plot Understanding
Introduces QCalEval benchmark showing best zero-shot VLM score of 72.3 on quantum calibration plots, with fine-tuning and in-context learning effects varying by model type.
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Reinforcement Learning for Robust Calibration of Multi-Qudit Quantum Gates
A hybrid optimal-control-plus-contextual-RL framework learns low-dimensional residual pulse corrections that preserve high-fidelity controlled-phase gates on two qutrits under realistic static model mismatch.