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Quantum control is valuable for various quantum technologies such as high-fidelity gates for universal quantum computing, adaptive quantum-enhanced metrology, and ultra-cold atom manipulation. Although supervised machine learning and reinforcement learning are widely used for optimizing control parameters in classical systems, quantum control for parameter optimization is mainly pursued via gradient-based...
Reinforcement learning algorithms have been shown to generate procedures for executing quantum control tasks to desired performances. Although reinforcement learning has been effective, its robustness in generating quantum control procedures under general noise condition needs to be tested. Here we consider adaptive quantum-enhanced interferometric phase estimation as a case study in quantum feedback...
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