Resource-efficient adaptive Bayesian tracking of magnetic fields with a quantum sensor.

Area of Science:

• Quantum sensing
• Nanoscale magnetic field mapping
• Diamond nitrogen-vacancy (NV) centers

Background:

• Single-spin quantum sensors, like those using NV centers in diamond, enable nanoscale magnetic field mapping.
• Rapidly changing magnetic fields necessitate minimizing total sensing time for efficient applications.
• Existing Bayesian estimation and adaptive optimization protocols reduce measurements but face computational speed limitations for real-time updates.

Purpose of the Study:

• To address the computational bottleneck in Bayesian quantum sensing protocols.
• To accelerate real-time magnetic field estimation by improving computational speed.
• To enhance the tracking accuracy of quantum sensors in dynamic magnetic environments.

Main Methods:

• Implementation of an approximate Bayesian estimation technique.
• Approximation of probability distributions using a finite sum of Gaussian functions.
• Characterization of magnetic field probability distributions with a reduced parameter set (typically <10 parameters).

Main Results:

• Achieved a tenfold reduction in computation time compared to existing approaches.
• Demonstrated that Gaussian approximations can effectively represent magnetic field probability distributions.
• Observed superior tracking accuracy with the Gaussian protocol in specific regimes (e.g., T2*=1μs), despite minor computation time gains.

Conclusions:

• The proposed Gaussian approximation method significantly enhances the computational speed of Bayesian quantum sensing.
• This approach enables faster real-time updates for adaptive experiment optimization in magnetic field sensing.
• The method offers improved tracking accuracy, particularly in dynamic magnetic field scenarios, making quantum sensors more effective.