NVRNet uses pretrained simulation-based U-Nets with attention and parameter-efficient adapters, followed by a transformer estimator, to reconstruct clean Ramsey waveforms and infer hyperfine parameters from minimal-sweep experimental data, achieving 0.44-0.67x noise reduction and 0.10-0.19 FFT error
Girshick, Fast r-cnn, inProceedings of the IEEE inter- national conference on computer vision(2015) pp
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
quant-ph 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Fast Single Nitrogen-Vacancy Center Ramsey Characterization using a Physics-Informed Neural Network
NVRNet uses pretrained simulation-based U-Nets with attention and parameter-efficient adapters, followed by a transformer estimator, to reconstruct clean Ramsey waveforms and infer hyperfine parameters from minimal-sweep experimental data, achieving 0.44-0.67x noise reduction and 0.10-0.19 FFT error