  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.
