Singlet-triplet-state readout in silicon-metal-oxide-semiconductor double quantum dots

TL;DR

This paper demonstrates high-fidelity singlet-triplet state readout in silicon quantum dots and introduces machine learning to improve accuracy beyond traditional threshold methods, especially for relaxed triplet states.

Contribution

It presents a machine learning-based readout method that surpasses threshold-based techniques in accuracy and relaxation independence for singlet-triplet state classification.

Findings

Achieved 97.59% fidelity with threshold method

Machine learning improves fidelity to 99.67%

Machine learning reduces misjudgment of relaxed triplet states

Abstract

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.