Papers › Fast and High-Fidelity Readout of Single Trapped-Ion Qubit via Machine Learning Methods
Fast and High-Fidelity Readout of Single Trapped-Ion Qubit via Machine Learning Methods
Zi-Han Ding, Jin-Ming Cui, Yun-Feng Huang, Chuan-Feng Li, Tao Tu, Guang-Can Guo
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In this work, we introduce machine learning methods to implement readout of a single qubit on ¹⁷¹Yb⁺ 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 10⁵ magnitude trials) within 171 μs is demonstrated on the embedded hardware system for ¹⁷¹Yb⁺ ion trap.
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