A model-free quantum stabilization framework uses sign-based Lyapunov descent, adaptive gains, and finite-difference LaSalle analogue to guarantee asymptotic stability in drift-free cases and practical ISS with unknown drift and noise.
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SpinTune applies reinforcement learning to discover adaptive dynamical decoupling sequences that outperform standard methods at preserving coherence in simulated Carbon-13 spin bath environments.
A multi-task SAC RL model discovers control pulses, evolution time, and segment numbers for 51 open quantum Hamiltonians, achieving high fidelity state transfer with better robustness to noise than GRAPE.
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.
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Model-Free Quantum Stabilization via Finite-Difference Lyapunov Control
A model-free quantum stabilization framework uses sign-based Lyapunov descent, adaptive gains, and finite-difference LaSalle analogue to guarantee asymptotic stability in drift-free cases and practical ISS with unknown drift and noise.
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SpinTune: Improving the Reliability of Quantum Sensor Networks for Practical Quantum-Classical Utility
SpinTune applies reinforcement learning to discover adaptive dynamical decoupling sequences that outperform standard methods at preserving coherence in simulated Carbon-13 spin bath environments.
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Adaptive Reinforcement Learning for Robust Open Quantum System Control: A Multi-Task Framework with Temporal Optimization
A multi-task SAC RL model discovers control pulses, evolution time, and segment numbers for 51 open quantum Hamiltonians, achieving high fidelity state transfer with better robustness to noise than GRAPE.
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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.