Robust quantum dots charge autotuning using neural network uncertainty
This addresses a key scaling challenge in quantum dot technologies by reducing human intervention in qubit tuning.
This study developed a machine learning method to automate charge tuning of semiconductor spin qubits, achieving over 99% success rate in optimal cases with more than 10% improvement directly from uncertainty exploitation.
This study presents a machine-learning-based procedure to automate the charge tuning of semiconductor spin qubits with minimal human intervention, addressing one of the significant challenges in scaling up quantum dot technologies. This method exploits artificial neural networks to identify noisy transition lines in stability diagrams, guiding a robust exploration strategy leveraging neural networks' uncertainty estimations. Tested across three distinct offline experimental datasets representing different single quantum dot technologies, the approach achieves over 99% tuning success rate in optimal cases, where more than 10% of the success is directly attributable to uncertainty exploitation. The challenging constraints of small training sets containing high diagram-to-diagram variability allowed us to evaluate the capabilities and limits of the proposed procedure.