Detection states of ions in a Paul trap via conventional and quantum machine learning algorithms

TL;DR

This paper explores the use of conventional and quantum machine learning algorithms to detect the quantum states of ions in a Paul trap, achieving high fidelities and advancing quantum measurement techniques.

Contribution

It introduces novel quantum and classical machine learning methods for ion state detection, demonstrating their effectiveness and potential for ultrahigh-fidelity quantum measurements.

Findings

Support vector machine and photon statistics methods achieved perfect fidelity.

Quantum annealing approach also achieved perfect fidelity.

Methods outperform standard techniques in ion state detection.

Abstract

Trapped ions are among the leading platforms for quantum technologies, particularly in the field of quantum computing. Detecting states of trapped ions is essential for ensuring high-fidelity readouts of quantum states. In this work, we develop and benchmark a set of methods for ion quantum state detection using images obtained by a highly sensitive camera. By transforming the images from the camera and applying conventional and quantum machine learning methods, including convolution, support vector machine (classical and quantum), and quantum annealing, we demonstrate a possibility to detect the positions and quantum states of ytterbium ions in a Paul trap. Quantum state detection is performed with an electron shelving technique: depending on the quantum state of the ion its fluorescence under the influence of a 369.5 nm laser beam is either suppressed or not. We estimate fidelities for conventional and quantum detection techniques. In particular, conventional algorithms for detecting $^{171}$Yb$^{+}$, such as the support vector machine and photon statistics-based method,as well as our quantum annealing-based approach, have achieved perfect fidelity, which is beneficial compared to standard techniques. This result may pave the way for ultrahigh-fidelity detection of trapped ions via conventional and quantum machine learning techniques.