Autonomous identification of quantum dot device failure modes

Gate-defined quantum dots have appealing attributes as a quantum computing platform, however near-term devices possess a range of possible imperfections. Recent efforts have introduced synthetic noise into simulated data[1,2] and a framework to avoid failures when tuning up nonideal devices [2]. However, resolving certain failure modes requires targeted identification of specific nonidealities. By incorporating into a quantum dot simulator sources of physical imperfections that go beyond noise, we expand the framework proposed in Ref. [2] to identify common defects and enable autonomous targeted device recalibration. Among others, we’re developing systems to autonomously flag devices with unintended dots near the operating regime and for identifying high levels of telegraph noise. These autonomous systems will enable both high throughput screening of quantum dot devices as well as more reliable tuning to a regime suitable for qubit operations.

[1] J. Darulova, et al., Evaluation of synthetic and experimental training data in supervised machine learning applied to charge state detection of quantum dots, arxiv:2005.08131 (2020).

[2] J. Ziegler, et al., Towards Robust Autotuning of Noisy Quantum Dot Devices, arxiv:2108.00043 (2021).