标题： $\varphi$-Adapt：一种物理信息自适应学习方法用于二维量子材料的发现 显示英文标题

标题： $\varphi$-Adapt: A Physics-Informed Adaptation Learning Approach to 2D Quantum Material Discovery

摘要： 表征量子薄片是量子硬件工程中的关键步骤，因为这些薄片的质量直接影响量子比特的性能。 尽管用于识别二维量子薄片的计算机视觉方法已经出现，但它们在估计薄片厚度方面仍然面临重大挑战。 这些挑战包括数据有限、泛化能力差、对领域偏移敏感以及缺乏物理可解释性。 在本文中，我们引入了首个物理信息适应学习方法，以克服这些障碍。 我们重点关注两个主要问题，即数据稀缺性和泛化能力。 首先，我们提出了一种新的合成数据生成框架，可以在各种材料和配置中生成多样化的量子薄片样本，从而减少对耗时的手动收集的需求。 其次，我们提出了$\varphi$-Adapt，这是一种物理信息适应方法，弥合了在合成数据上训练的模型与在现实环境中部署的模型之间的性能差距。 实验结果表明，我们的方法在多个基准测试中实现了最先进的性能，优于现有方法。 我们提出的方法推进了基于物理的建模和领域适应的整合。 它还解决了利用合成数据进行现实世界二维材料分析中的一个关键空白，为深度学习和材料科学社区提供了有影响力的工具。 显示英文摘要

摘要： Characterizing quantum flakes is a critical step in quantum hardware engineering because the quality of these flakes directly influences qubit performance. Although computer vision methods for identifying two-dimensional quantum flakes have emerged, they still face significant challenges in estimating flake thickness. These challenges include limited data, poor generalization, sensitivity to domain shifts, and a lack of physical interpretability. In this paper, we introduce one of the first Physics-informed Adaptation Learning approaches to overcome these obstacles. We focus on two main issues, i.e., data scarcity and generalization. First, we propose a new synthetic data generation framework that produces diverse quantum flake samples across various materials and configurations, reducing the need for time-consuming manual collection. Second, we present $\varphi$-Adapt, a physics-informed adaptation method that bridges the performance gap between models trained on synthetic data and those deployed in real-world settings. Experimental results show that our approach achieves state-of-the-art performance on multiple benchmarks, outperforming existing methods. Our proposed approach advances the integration of physics-based modeling and domain adaptation. It also addresses a critical gap in leveraging synthesized data for real-world 2D material analysis, offering impactful tools for deep learning and materials science communities.