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Quantum magnetometry enhanced by machine learning
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DOI：10.1088/2058-9565/ae3acf.png)
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摘要
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Quantum optimal control in color center physics plays a crucial role in advancing sensor technology. This study focuses on optimizing microwave pulse shapes within a Ramsey sequence for nitrogen-vacancy centers to enhance sensor sensitivity and signal detection capabilities. We compare state-of-the-art optimization methods, including the dressed chopped randomized basis Nelder–Mead algorithm and covariance matrix adaptation evolutionary strategy, and extend our search to machine learning approaches, such as Gaussian processes and artificial neural networks. These machine learning techniques are specifically designed to provide robust and global solutions that can rapidly adapt to changing environmental conditions. Our results demonstrate more than a sixfold increase in convergence speed compared to conventional methods and considerable contrast improvements with a limited retraining set of 72 samples. Furthermore, we demonstrate that the optimized Ramsey contrast translates into a significant enhancement in the signal-to-noise ratio for detecting synthetic magnetic heart signals. This highlights the potential of machine learning-driven quantum optimal control for developing more flexible, adaptive, and efficient quantum sensing solutions in real-world scenarios.
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5
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1.4K
被引数:
5.1K
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Deep-neural-network-based magnetic-field acquisition method for quantum sensing with nitrogen-vacancy centers基于深度神经网络的氮 vacancy 中心量子传感磁场获取方法
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分享收藏Universal enhancement of the optical readout fidelity of single electron spins at nitrogen-vacancy centers in diamond.png)
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PHYSICAL REVIEW BIF3.7
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