Toward Robust Autotuning of Noisy Quantum Dot Devices

TL;DR

This paper introduces a robust machine learning framework for autonomous tuning of quantum dot devices that effectively handles noisy data by integrating a data quality control module with a state classifier.

Contribution

It presents a novel framework combining data quality control with ML-based state classification to improve autotuning robustness in noisy quantum dot experiments.

Findings

Inclusion of synthetic noise in training improves classifier accuracy to 95%.

Data quality control effectively filters unreliable data, preventing failures.

The framework enhances autonomous tuning reliability in noisy conditions.

Abstract

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 "gatekeeper" 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.