Real-time frequency estimation of a qubit without single-shot-readout

TL;DR

This paper demonstrates real-time frequency estimation of a qubit using a non-adaptive phase estimation algorithm with a binomial distribution approach, improving accuracy over traditional majority voting methods in non-single-shot-readout sensors.

Contribution

First implementation of real-time non-adaptive phase estimation with binomial distribution on a non-SSR sensor, enhancing measurement accuracy and proposing an adaptive protocol for further improvements.

Findings

Binomial distribution method outperforms majority voting in accuracy.

Adaptive algorithm can further reduce sensing time and improve precision.

Experimental validation with nitrogen-vacancy center in diamond.

Abstract

Quantum sensors can potentially achieve the Heisenberg limit of sensitivity over a large dynamic range using quantum algorithms. The adaptive phase estimation algorithm (PEA) is one example that was proven to achieve such high sensitivities with single-shot readout (SSR) sensors. However, using the adaptive PEA on a non-SSR sensor is not trivial due to the low contrast nature of the measurement. The standard approach to account for the averaged nature of the measurement in this PEA algorithm is to use a method based on `majority voting'. Although it is easy to implement, this method is more prone to mistakes due to noise in the measurement. To reduce these mistakes, a binomial distribution technique from a batch selection was recently shown theoretically to be superior, as all ranges of outcomes from an averaged measurement are considered. Here we apply, for the first time, real-time non-adaptive PEA on a non-SSR sensor with the binomial distribution approach. We compare the mean square error of the binomial distribution method to the majority-voting approach using the nitrogen-vacancy center in diamond at ambient conditions as a non-SSR sensor. Our results suggest that the binomial distribution approach achieves better accuracy with the same sensing times. To further shorten the sensing time, we propose an adaptive algorithm that controls the readout phase and, therefore, the measurement basis set. We show by numerical simulation that adding the adaptive protocol can further improve the accuracy in a future real-time experiment.