论文标题
通过机器学习优化量子点量子
Optimization of Quantum-dot Qubit Fabrication via Machine Learning
论文作者
论文摘要
精确的纳米化代表着开发用于实用量子计算的半导体量子点量子矩的关键挑战。在这里，我们设计和训练卷积神经网络以解释在线扫描电子显微照片并量化影响器件功能的定性特征。通过在五维设计空间中优化模型光刻过程，并通过展示一种解决光刻近距离效应的新方法来体现高通量策略。目前的结果强调了机器学习的好处，以开发健壮的过程，缩短开发周期以及在量子制造过程中执行质量控制。
Precise nanofabrication represents a critical challenge to developing semiconductor quantum-dot qubits for practical quantum computation. Here, we design and train a convolutional neural network to interpret in-line scanning electron micrographs and quantify qualitative features affecting device functionality. The high-throughput strategy is exemplified by optimizing a model lithographic process within a five-dimensional design space and by demonstrating a new approach to address lithographic proximity effects. The present results emphasize the benefits of machine learning for developing robust processes, shortening development cycles, and enforcing quality control during qubit fabrication.
