Optimal and Variational Multiparameter Quantum Metrology and Vector-Field Sensing

We study multiparameter sensing of two-dimensional (2D) and three-dimensional (3D) vector fields within the Bayesian framework for SU(2) quantum interferometry. We establish a method to determine the optimal quantum sensor, which establishes the fundamental limit on the precision of simultaneously estimating multiple parameters with an N-atom sensor. Keeping current experimental platforms in mind, we present sensors that have limited entanglement capabilities and yet significantly outperform sensors that operate without entanglement and approach the optimal quantum sensor in terms of performance. Furthermore, we show how these sensors can be implemented on current programmable quantum sensors with variational quantum circuits by minimizing a metrological cost function. The resulting circuits prepare tailored entangled states and perform measurements in an appropriate entangled basis to realize the best-possible quantum sensor given the native entangling resources available on a given sensor platform. Notable examples include a 2D and 3D quantum compass and a 2D sensor that provides a scalable improvement over unentangled sensors. Our results on optimal and variational multiparameter quantum metrology are useful for advancing precision measurements in fundamental science and ensuring the stability of quantum computers, which can be achieved through the incorporation of optimal quantum sensors in a quantum feedback loop.

Automated summary

We study the multiparameter sensing of 2D and 3D vector fields using SU(2) quantum interferometry in the Bayesian framework. We develop a method to determine the optimal quantum sensor, which sets the fundamental limit on precision for simultaneously estimating multiple parameters with an N-atom sensor. We propose sensors with limited entanglement capabilities that outperform sensors without entanglement and approach the optimal quantum sensor in terms of performance.