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

Understand

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.