QFlow lite dataset

QFlow lite数据集是由联合量子研究所创建的，包含1001个模拟量子点设备特性的数据集。该数据集通过模拟电流、电荷传感器响应与应用静电门电压之间的关系，为训练卷积神经网络提供了基础。数据集的创建旨在解决量子点设备调谐的自动化问题，通过机器学习算法实现设备状态的自动识别和调谐，适用于量子计算和通信技术领域。The QFlow lite dataset was created by the Joint Quantum Institute, which includes 1001 datasets that simulate the characteristics of quantum dot devices. This dataset serves as a foundational resource for training convolutional neural networks by modeling the relationships among electrical current, charge sensor responses, and applied electrostatic gate voltages. Developed to address the automation issue of quantum dot device tuning, the dataset enables automatic recognition and tuning of device states through machine learning algorithms, and is applicable to the fields of quantum computing and quantum communication technologies.