QDFlow生成量子点设备的合成数据，助力机器学习训练与器件研究。
QDFlow: A Python package for physics simulations of quantum dot devices
- 基于自洽托马斯-费米求解器与电容模型，模拟多量子点阵列
- 生成带真实标签的电荷稳定性图与射线数据，逼近实验结果
- 支持可调参数与噪声模型，适用于机器学习数据集构建
近年来，机器学习加速了量子点（QD）器件的校准与操作进展。然而，多数机器学习方法依赖于大规模、具有代表性的数据集，涵盖高、低质量数据及标注了器件状态关键特征的标签。由于实验数据获取困难、测量带宽有限且标注耗时，这类数据集难以构建。QDFlow 是一个开源的多量子点阵列物理模拟器，可生成带有真实标签的逼真合成数据。该工具结合自洽托马斯-费米求解器、动态电容模型和灵活的噪声模块，能够模拟电荷稳定性图与基于射线的数据，结果高度接近实际实验。通过大量可调节参数和可定制噪声模型，QDFlow 支持构建大规模、多样化的数据集，服务于机器学习开发、基准测试与量子器件研究。
原文摘要 · Abstract (English)
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 availability, slow measurement bandwidths, and the labor-intensive nature of labeling. QDFlow is an open-source physics simulator for multi-QD arrays that generates realistic synthetic data with ground-truth labels. QDFlow combines a self-consistent Thomas-Fermi solver, a dynamic capacitance model, and flexible noise modules to simulate charge stability diagrams and ray-based data that closely resemble experimental results. With an extensive set of parameters that can be varied and customizable noise models, QDFlow supports the creation of large, diverse datasets for ML development, benchmarking, and quantum device research.}}
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。