



01880nas a2200229   4500008004100000245005800041210005800099260001500157490000700172520123000179100002001409700002101429700001701450700002601467700002001493700001701513700001801530700001901548700002201567700002401589856003701613       2022                        eng d00aToward Robust Autotuning of Noisy Quantum dot Devices0 aToward Robust Autotuning of Noisy Quantum dot Devices  c02/26/20220 v173 a<p>The current autotuning approaches for quantum dot (QD) devices, while showing some success, lack an assessment of data reliability. This leads to unexpected failures when noisy or otherwise low-quality data is processed by an autonomous system. In this work, we propose a framework for robust autotuning of QD devices that combines a machine learning (ML) state classifier with a data quality control module. The data quality control module acts as a &quot;gatekeeper&quot; system, ensuring that only reliable data are processed by the state classifier. Lower data quality results in either device recalibration or termination. To train both ML systems, we enhance the QD simulation by incorporating synthetic noise typical of QD experiments. We confirm that the inclusion of synthetic noise in the training of the state classifier significantly improves the performance, resulting in an accuracy of 95.0(9) % when tested on experimental data. We then validate the functionality of the data quality control module by showing that the state classifier performance deteriorates with decreasing data quality, as expected. Our results establish a robust and flexible ML framework for autonomous tuning of noisy QD devices.</p>1 aZiegler, Joshua1 aMcJunkin, Thomas1 aJoseph, E.S.1 aKalantre, Sandesh, S.1 aHarpt, Benjamin1 aSavage, D.E.1 aLagally, M.G.1 aEriksson, M.A.1 aTaylor, Jacob, M.1 aZwolak, Justyna, P.  uhttps://arxiv.org/abs/2108.00043