Weighing unequal parameter importance and measurement expense in adaptive quantum sensing

Area of Science:

• Physics
• Quantum Sensing
• Experimental Design

Background:

• Accurate and rapid measurement of physical model parameters is crucial.
• Traditional methods often determine instrument settings a priori, limiting efficiency.
• Adaptive experiment design offers real-time optimization based on potential information gain.

Purpose of the Study:

• Introduce two novel algorithms to improve adaptive experiment design.
• Enhance the efficiency and sensitivity of experimental measurements.
• Apply and validate these algorithms in quantum sensing, specifically magnetometry.

Main Methods:

• Developed two Bayesian-based algorithms for adaptive experiment design.
• Algorithm 1 prioritizes learning the most significant model parameters.
• Algorithm 2 incorporates measurement cost, prioritizing lower-cost information acquisition.

Main Results:

• Demonstrated significant improvements in experimental efficiency and sensitivity.
• Achieved an almost five-fold enhancement in magnetic field sensitivity.
• Validated the algorithms' effectiveness using nitrogen-vacancy centers in diamond for quantum magnetometry.

Conclusions:

• The new adaptive experiment design algorithms substantially improve measurement efficiency.
• These algorithms offer a powerful approach for optimizing quantum sensing experiments.
• The developed methods lead to remarkable gains in sensitivity for magnetometry applications.