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
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4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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DFT calculations provide electron and hole capture coefficients for N_C and NV defects, matching experiment for N_C and showing excited-state pathways dominate for NV.
Framework extracts optical dipole polarization and strain-orbital coupling from ensemble spectra of erbium in silicon and verifies cavity-coupling predictions on single ions.
Spectator qubits with real-time decision making and feedforward mitigate dephasing in an NV-center quantum network node, improving memory fidelity during remote entanglement generation.
citing papers explorer
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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
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Charge dynamics at nitrogen impurities and nitrogen-vacancy centers in diamond
DFT calculations provide electron and hole capture coefficients for N_C and NV defects, matching experiment for N_C and showing excited-state pathways dominate for NV.
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A unified framework for determining transition dipole polarization in solid-state spin defects
Framework extracts optical dipole polarization and strain-orbital coupling from ensemble spectra of erbium in silicon and verifies cavity-coupling predictions on single ions.
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Quantum-network nodes with real-time noise mitigation using spectator qubits
Spectator qubits with real-time decision making and feedforward mitigate dephasing in an NV-center quantum network node, improving memory fidelity during remote entanglement generation.