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

• URL: http://arxiv.org/abs/2507.05184v1
• Date: Mon, 07 Jul 2025 16:40:35 GMT
• Title: $\varphi$-Adapt: A Physics-Informed Adaptation Learning Approach to 2D Quantum Material Discovery
• Authors: Hoang-Quan Nguyen, Xuan Bac Nguyen, Sankalp Pandey, Tim Faltermeier, Nicholas Borys, Hugh Churchill, Khoa Luu,
• Abstract summary: Characterizing quantum flakes is a critical step in quantum hardware engineering because the quality of these flakes directly influences qubit performance.<n>Computer vision methods for identifying two-dimensional quantum flakes have emerged, but they still face significant challenges in estimating flake thickness.<n>We introduce one of the first Physics-informed Adaptation Learning approaches to overcome these obstacles.
• Score: 7.615935942148471
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• PhysiX: A Foundation Model for Physics Simulations [27.359872113159405]
  We introduce PhysiX, the first large-scale foundation model for physics simulation.<n>We show that PhysiX effectively addresses the data bottleneck, outperforming task-specific baselines.<n>Our results indicate that knowledge learned from natural videos can be successfully transferred to physics simulation.
  arXiv  Detail & Related papers  (2025-06-21T18:10:12Z)
• Adversarial Disentanglement by Backpropagation with Physics-Informed Variational Autoencoder [0.0]
  Inference and prediction under partial knowledge of a physical system is challenging.<n>We propose a physics-informed variational autoencoder architecture that combines the interpretability of physics-based models with the flexibility of data-driven models.
  arXiv  Detail & Related papers  (2025-06-16T16:18:25Z)
• PhysGaia: A Physics-Aware Dataset of Multi-Body Interactions for Dynamic Novel View Synthesis [62.283499219361595]
  PhysGaia is a physics-aware dataset specifically designed for Dynamic Novel View Synthesis (DyNVS)<n>Our dataset provides complex dynamic scenarios with rich interactions among multiple objects.<n>PhysGaia will significantly advance research in dynamic view synthesis, physics-based scene understanding, and deep learning models integrated with physical simulation.
  arXiv  Detail & Related papers  (2025-06-03T12:19:18Z)
• The Sound of Water: Inferring Physical Properties from Pouring Liquids [85.30865788636386]
  We study the connection between audio-visual observations and the underlying physics of pouring liquids.<n>Our objective is to automatically infer physical properties such as the liquid level, the shape and size of the container, the pouring rate and the time to fill.
  arXiv  Detail & Related papers  (2024-11-18T01:19:37Z)
• PhyRecon: Physically Plausible Neural Scene Reconstruction [81.73129450090684]
  We introduce PHYRECON, the first approach to leverage both differentiable rendering and differentiable physics simulation to learn implicit surface representations. Central to this design is an efficient transformation between SDF-based implicit representations and explicit surface points. Our results also exhibit superior physical stability in physical simulators, with at least a 40% improvement across all datasets.
  arXiv  Detail & Related papers  (2024-04-25T15:06:58Z)
• Physics-Enhanced Multi-fidelity Learning for Optical Surface Imprint [1.0878040851638]
  We propose a novel method to use multi-fidelity neural networks (MFNN) to solve this inverse problem. We build up the NN model via pure simulation data, and then bridge the sim-to-real gap via transfer learning. Considering the difficulty of collecting real experimental data, we use NN to dig out the unknown physics and also implant the known physics into the transfer learning framework.
  arXiv  Detail & Related papers  (2023-11-17T01:55:15Z)
• Physics-informed Variational Autoencoders for Improved Robustness to Environmental Factors of Variation [0.6384650391969042]
  p$3$VAE is a variational autoencoder that integrates prior physical knowledge about the latent factors of variation related to the data acquisition conditions.<n>We introduce a semi-supervised learning algorithm that strikes a balance between the machine learning part and the physics part.
  arXiv  Detail & Related papers  (2022-10-19T09:32:15Z)
• Learning Physical Dynamics with Subequivariant Graph Neural Networks [99.41677381754678]
  Graph Neural Networks (GNNs) have become a prevailing tool for learning physical dynamics. Physical laws abide by symmetry, which is a vital inductive bias accounting for model generalization. Our model achieves on average over 3% enhancement in contact prediction accuracy across 8 scenarios on Physion and 2X lower rollout MSE on RigidFall.
  arXiv  Detail & Related papers  (2022-10-13T10:00:30Z)
• DeepPhysics: a physics aware deep learning framework for real-time simulation [0.0]
  We propose a solution to simulate hyper-elastic materials using a data-driven approach. A neural network is trained to learn the non-linear relationship between boundary conditions and the resulting displacement field. The results show that our network architecture trained with a limited amount of data can predict the displacement field in less than a millisecond.
  arXiv  Detail & Related papers  (2021-09-17T12:15:47Z)
• Quantum-tailored machine-learning characterization of a superconducting qubit [50.591267188664666]
  We develop an approach to characterize the dynamics of a quantum device and learn device parameters. This approach outperforms physics-agnostic recurrent neural networks trained on numerically generated and experimental data. This demonstration shows how leveraging domain knowledge improves the accuracy and efficiency of this characterization task.
  arXiv  Detail & Related papers  (2021-06-24T15:58:57Z)
• Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling [86.9726984929758]
  We focus on the integration of incomplete physics models into deep generative models. We propose a VAE architecture in which a part of the latent space is grounded by physics. We demonstrate generative performance improvements over a set of synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2021-02-25T20:28:52Z)
• Neural network quantum state tomography in a two-qubit experiment [52.77024349608834]
  Machine learning inspired variational methods provide a promising route towards scalable state characterization for quantum simulators. We benchmark and compare several such approaches by applying them to measured data from an experiment producing two-qubit entangled states. We find that in the presence of experimental imperfections and noise, confining the variational manifold to physical states greatly improves the quality of the reconstructed states.
  arXiv  Detail & Related papers  (2020-07-31T17:25:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.