Comparison of Atom Detection Algorithms for Neutral Atom Quantum Computing

TL;DR

This study compares various atom detection algorithms in neutral atom quantum computing, evaluating their accuracy and speed using synthetic images and theoretical bounds, to identify optimal methods for different scenarios.

Contribution

It provides a comprehensive comparison of detection algorithms, including a novel analysis using the Cramér-Rao bound, and suggests trade-offs between accuracy and computational complexity.

Findings

The non-linear least-squares solver performs best in accuracy.

Simple algorithms are faster but less precise.

The Cramér-Rao bound sets an upper limit on detection accuracy.

Abstract

In neutral atom quantum computers, readout and preparation of the atomic qubits are usually based on fluorescence imaging and subsequent analysis of the acquired image. For each atom site, the brightness or some comparable metric is estimated and used to predict the presence or absence of an atom. Across different setups, we can see a vast number of different approaches used to analyze these images. Often, the choice of detection algorithm is either not mentioned at all or it is not justified. We investigate several different algorithms and compare their performance in terms of both precision and execution run time. To do so, we rely on a set of synthetic images across different simulated exposure times with known occupancy states. Since the use of simulation provides us with the ground truth of atom site occupancy, we can easily state precise error rates and variances of the reconstructed property. To also rule out the possibility of better algorithms existing, we calculated the Cram\'er-Rao bound in order to establish an upper limit that even a perfect estimator cannot outperform. As the metric of choice, we used the number of photonelectrons that can be contributed to a specific atom site. Since the bound depends on the occupancy of neighboring sites, we provide the best and worst cases, as well as a half filled one. Our comparison shows that of our tested algorithms, a global non-linear least-squares solver that uses the optical system's PSF to return a each sites' number of photoelectrons performed the best, on average crossing the worst-case bound for longer exposure times. Its main drawback is its huge computational complexity and, thus, required calculation time. We manage to somewhat reduce this problem, suggesting that its use may be viable. However, our study also shows that for cases where utmost speed is required, simple algorithms may be preferable.