Neural network based deep learning analysis of semiconductor quantum dot qubits for automated control
Jacob Taylor, S. Das Sarma
Abstract
In this paper, we introduce a methodology that employs machine learning, specifically convolutional neural networks (CNNs), to discern the disorder landscape in the parameters of the disordered extended Hubbard model underlying the semiconductor quantum dot spin qubit architectures. Our approach, which we demonstrate with numerically simulated devices, takes advantage of currently used experimentally obtainable charge stability diagrams from neighboring quantum dot pairs, enabling the CNN to accurately identify disorder in each parameter of the extended Hubbard model. Remarkably, our CNN can process site-specific disorder in Hubbard parameters, including variations in hopping constants, on-site potentials (gate voltages), and both intrasite and intersite Coulomb terms. This advancement facilitates the prediction of spatially dependent disorder across all parameters simultaneously with high accuracy (${R}^{2}>0.994$) and fewer parameter constraints, marking a significant improvement over previous methods that were focused only on analyzing on-site potentials at low coupling. Furthermore, our approach allows for the tuning of five or more quantum dots at a time, effectively addressing the often-overlooked issue of crosstalk. Not only does our method streamline the tuning process, potentially enabling fully automated adjustments, but it also introduces a ``no trust'' verification method to rigorously validate the neural network's predictions. Ultimately, this paper aims to lay the groundwork for generalizing our method to tackle a broad spectrum of physical problems.