Variational optimization on dipolar spin chains reaches 0.92 of the quantum Fisher information benchmark for joint magnetometry and gradiometry, delivering a 4.2x advantage over the standard quantum limit.
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2026 2verdicts
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A physics-informed DIP method using a simple convolutional autoencoder reconstructs complex in-plane magnetization from NV magnetometry, with optimal mask orientation improving SNR by up to 3 dB.
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Variational Joint Magnetometry and Gradiometry on Dipolar Spin Chains
Variational optimization on dipolar spin chains reaches 0.92 of the quantum Fisher information benchmark for joint magnetometry and gradiometry, delivering a 4.2x advantage over the standard quantum limit.
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Physics-Informed Deep Image Prior Reconstruction of In-Plane Magnetization from Scanning NV Magnetometry
A physics-informed DIP method using a simple convolutional autoencoder reconstructs complex in-plane magnetization from NV magnetometry, with optimal mask orientation improving SNR by up to 3 dB.