Fast and High-Fidelity Readout of Single Trapped-Ion Qubit via Machine Learning Methods

TL;DR

This paper demonstrates that machine learning techniques, including neural networks, significantly improve the speed and fidelity of single-ion qubit readout in trapped-ion systems, achieving over 99.5% fidelity in under 200 microseconds.

Contribution

It introduces machine learning algorithms for qubit readout, showing they outperform traditional methods in speed and accuracy, and implements these algorithms on embedded hardware.

Findings

Neural networks achieve 99% fidelity in half the time of traditional methods.

Embedded hardware implementation yields 99.5% fidelity within 171 microseconds.

Machine learning methods are more robust and faster for qubit readout.

Abstract

In this work, we introduce machine learning methods to implement readout of a single qubit on $^{171}\mathrm{Yb^{+}}$ trapped-ion system. Different machine learning methods including convolutional neural networks and fully-connected neural networks are compared with traditional methods in the tests. The results show that machine learning methods have higher fidelity, more robust readout results in relatively short time. To obtain a 99% readout fidelity, neural networks only take half of the detection time needed by traditional threshold or maximum likelihood methods. Furthermore, we implement the machine learning algorithms on hardware-based field-programmable gate arrays and an ARM processor. An average readout fidelity of 99.5% (with $10^5$ magnitude trials) within 171 $\mu$s is demonstrated on the embedded hardware system for $^{171}\mathrm{Yb^{+}}$ ion trap.