Singlet-triplet-state readout in silicon metal-oxide-semiconductor double quantum dots
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 noise-resilient and 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 the machine-learning method, better than the threshold method of 97.54%, which is consistent with the experimental result. This work indicates that the 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.
- Publication:
-
Physical Review Applied
- Pub Date:
- March 2024
- DOI:
- 10.1103/PhysRevApplied.21.034022
- arXiv:
- arXiv:2309.09723
- Bibcode:
- 2024PhRvP..21c4022M
- Keywords:
-
- Condensed Matter - Mesoscale and Nanoscale Physics;
- Quantum Physics
- E-Print:
- 11 pages,11 figures,