QDFlow: A Python package for physics simulations of quantum dot devices
Donovan L. Buterakos, Sandesh S. Kalantre, Joshua Ziegler, Jacob M Taylor, Justyna P. Zwolak
机器学习(ML)的最新进展加速了校准和操作量子点(QD)设备的进展。 然而,大多数ML方法依赖于访问大型具有代表性的数据集,旨在捕获实践中遇到的全谱数据质量,具有用于训练,基准测试和验证的高和低质量数据,标签捕获设备状态的关键特征。 由于数据可用性有限,测量带宽缓慢以及标签的劳动密集型性质,通过实验整理此类数据集具有挑战性。 QDFlow是一个用于多QD阵列的开源物理模拟器,可以生成具有地面真实标签的现实合成数据。 QDFlow结合了自立的Thomas-Fermi求解器,动态电容模型和灵活的噪声模块,以模拟电荷稳定性图和类似于实验的基于射线的数据。 凭借一系列可更改和可定制的噪声模型的广泛参数,QDFlow支持创建用于ML开发,基准测试和量子设备研究的大型,多样化的数据集。
Recent advances in machine learning (ML) have accelerated progress in calibrating and operating quantum dot (QD) devices. However, most ML approaches rely on access to large, representative datasets designed to capture the full spectrum of data quality encountered in practice, with both high- and low-quality data for training, benchmarking, and validation, with labels capturing key features of the device state. Collating such datasets experimentally is challenging due to limited data availabilit...