A randomized-response training framework that provably preserves individual fairness at initialization and at every training step, and is claimed to be much cheaper than verification-based certified training.
Quantum Co-Magnetometer Using Diamond Nitrogen-Vacancy Centers and Rubidium Cells
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abstract
Recent advances in chip scale magnetic quantum sensing have produced platforms that pair unprecedented sensitivity with extreme miniaturization. Here, we demonstrate a hybrid quantum sensor by combining Nitrogen-Vacancy (NV) centers in diamond with a rubidium (Rb) vapor cell, designed for precise magnetic field measurements and quantum exploration. The hybrid comagnetometer leverages the high resolution vector magnetic sensing of NV centers along with the high scalar field sensitivity of the Rb vapor, enhancing the estimation of the magnetic field in terms of magnitude, direction and spatial distribution. A micromachined mm scale vapor cell containing Rb atoms is paired with a bulk diamond, enabling optical and microwave control of both quantum systems for integrated field estimation. Simulations and experimental results confirm the improved accuracy of the system in magnetic field measurements, demonstrating a beyond 10 dB improvement. This NV and Rb platform offers a versatile route toward portable, sensitive magnetometry and opens new possibilities for integrated, multi-modal quantum sensing.
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cs.LG 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
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Correct-By-Construction: Certified Individual Fairness through Neural Network Training
A randomized-response training framework that provably preserves individual fairness at initialization and at every training step, and is claimed to be much cheaper than verification-based certified training.