基于神经网络的半导体量子点量子比特深度学习分析以实现自动化控制
Neural network based deep learning analysis of semiconductor quantum dot qubits for automated control
AI总结：
本研究提出一种基于卷积神经网络的新方法，利用电荷稳定图识别半导体量子点扩展Hubbard模型的多参数无序，实现多量子点同时调谐与结果验证，为自动化控制及更广泛物理问题应用奠基。
摘要：
机器学习为利用自动化算法改进物理学中存在噪声的无序器件提供了一个尚未被充分探索的途径。通过对包含物理器件（尤其是量子器件）中无序效应的模拟，研究人员有望了解无序势场分布，并基于这些见解对器件进行调谐。在本研究中，我们提出了一种新颖的方法，该方法采用机器学习（具体为卷积神经网络（CNNs））来识别半导体量子点自旋量子比特架构背后的无序扩展Hubbard模型参数中的无序势场分布。该技术利用实验可获得的相邻量子点对的电荷稳定图，使CNN能够准确识别扩展Hubbard模型各参数中的无序情况。值得注意的是，我们的CNN可以处理Hubbard参数中的位点特异性无序，包括跃迁常数、位点电势（栅极电压）以及位点内和位点间库仑项的变化。这一进展能够同时高精度（$R^2>0.994$）地预测所有参数的空间依赖性无序，且参数约束更少，相较于此前仅专注于分析低耦合下位点电势的方法有显著提升。此外，我们的方法可一次调谐五个或更多量子点，有效解决了常被忽视的串扰问题。我们的方法不仅简化了调谐过程，有望实现全自动调整，还引入了一种“不信任”验证方法，可严格验证神经网络的预测结果。最终，本研究旨在为将我们的方法推广应用于解决广泛的物理问题奠定基础。
英文摘要：
Machine learning offers a largely unexplored avenue for improving noisy disordered devices in physics using automated algorithms. Through simulations that include disorder in physical devices, particularly quantum devices, there is potential to learn about disordered landscapes and subsequently tune devices based on those insights. In this work, we introduce a novel 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. This technique takes advantage of 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 intra-site and inter-site 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 work aims to lay the groundwork for generalizing our method to tackle a broad spectrum of physical problems.
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