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

TL;DR

This paper introduces $unction$, a physics-informed adaptation learning method that improves 2D quantum material flake identification by addressing data scarcity and domain shift, achieving state-of-the-art results.

Contribution

It presents a novel synthetic data generation framework and a physics-informed adaptation approach to enhance model generalization in quantum flake analysis.

Findings

Achieves state-of-the-art performance on multiple benchmarks.

Outperforms existing methods in quantum flake identification.

Effectively bridges the gap between synthetic and real data.

Abstract

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.