High-fidelity singlet-triplet state readout is essential for large-scale quantum computing. However, the widely used threshold method of comparing a mean value with the fixed threshold will limit the judgment accuracy, especially for the relaxed triplet state, under the restriction of relaxation time and signal-to-noise ratio. Here, we achieve an enhanced latching readout based on Pauli spin blockade in a Si-MOS double quantum dot device and demonstrate an average singlet-triplet state readout fidelity of 97.59% by the threshold method. We reveal the inherent deficiency of the threshold method for the relaxed triplet state classification and introduce machine learning as a relaxation-independent readout method to reduce the misjudgment. The readout fidelity for classifying the simulated single-shot traces can be improved to 99.67% by machine learning method, better than the threshold method of 97.54% which is consistent with the experimental result. This work indicates that machine learning method can be a strong potential candidate for alleviating the restrictions of stably achieving high-fidelity and high-accuracy singlet-triplet state readout in large-scale quantum computing.
@article{arxiv.2309.09723,
title = {Singlet-triplet-state readout in silicon-metal-oxide-semiconductor double quantum dots},
author = {Rong-Long Ma and Sheng-Kai Zhu and Zhen-Zhen Kong and Tai-Ping Sun and Ming Ni and Yu-Chen Zhou and Yuan Zhou and Gang Luo and Gang Cao and Gui-Lei Wang and Hai-Ou Li and Guo-Ping Guo},
journal= {arXiv preprint arXiv:2309.09723},
year = {2024}
}