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Bayesian-Based Hybrid Method for Rapid Optimization of NV Center Sensors
Jiazhao Tian1, Ressa S Said2, Fedor Jelezko2
1School of Physics, Taiyuan University of Technology, Taiyuan 030024, China.
Sensors (Basel, Switzerland)
|March 30, 2023
Summary
We developed a new Bayesian estimation phase-modulated (B-PM) method for quantum sensing. This technique significantly reduces computation time and enhances the fidelity and coherence time of nitrogen-vacancy (NV) center sensors.
Area of Science:
- Quantum Sensing
- Quantum Information Science
- Materials Science
Background:
- Nitrogen-vacancy (NV) centers are leading quantum sensing platforms, particularly in biomedicine.
- Enhancing NV center sensor sensitivity under challenging conditions like broadening and drift is critical.
- Current quantum optimal control (QOC) methods are time-consuming and complex for practical applications.
Purpose of the Study:
- To introduce a novel, efficient method for coherent control of NV centers.
- To address the limitations of existing QOC techniques in terms of speed and complexity.
- To improve the performance of NV center-based quantum sensors.
Main Methods:
- Proposed the Bayesian estimation phase-modulated (B-PM) method.
- Applied B-PM to NV center ensemble state transformation.
- Utilized B-PM for AC magnetometry control pulse optimization.
Main Results:
- B-PM reduced computation time by over 90% compared to the standard Fourier basis (SFB) method.
- Average fidelity increased from 0.894 to 0.905 for NV center ensembles.
- Optimized pulses extended coherence time (T2) eight-fold versus rectangular pulses in magnetometry.
Conclusions:
- The B-PM method offers a significant advancement in efficient quantum control for NV centers.
- This approach enhances sensor performance and reduces experimental time.
- B-PM is a versatile algorithm applicable to various quantum systems and control strategies.
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