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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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.
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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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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.