Bayesian-based hybrid method for rapid optimization of NV center sensors

TL;DR

This paper introduces a Bayesian phase-modulated method that significantly accelerates quantum control optimization for NV center sensors, enhancing sensitivity and coherence time in quantum sensing applications.

Contribution

The paper presents a novel Bayesian-based control optimization method that reduces time consumption by over 90% and improves fidelity, advancing quantum sensing with NV centers.

Findings

Reduced optimization time by over 90% compared to traditional methods.

Increased average fidelity from 0.894 to 0.905.

Extended coherence time T2 by eight times in magnetometry applications.

Abstract

NV center is one of the most promising platforms in the field of quantum sensing. Magnetometry based on NV center, especially, has achieved a concrete development in regions of biomedicine and medical diagnostics. Improving the sensitivity of NV center sensor under wide inhomogeneous broadening and filed amplitude drift is one crucial issue of continuous concern, which relies on the coherent control of NV center with higher average fidelity. Quantum optimal control (QOC) methods provide access to this target, nevertheless the high time consumption of current methods due to the large number of needful sample points as well as the complexity of the parameter space has hindered their usability. In this paper we propose the Bayesian estimation phase-modulated (B-PM) method to tackle this problem. In the case of state transforming of NV center ensemble, the B-PM method reduces the time consumption by more than $90\%$ compared to the conventional standard Fourier base (SFB) method while increasing the average fidelity from $0.894$ to $0.905$. In AC magnetometry scenery, the optimized control pulse given by B-PM method achieves a eight-fold extension of the coherence time $T_2$ compared to rectangular $\pi$ pulse. Similar application can be made in other sensing situations. As a general algorithm, the B-PM method can be further extended to open- and closed-loop optimization of complex systems based on a variety of quantum platforms.