Title: Quantum Noise Tomography with Physics-Informed Neural Networks Show Chinese title

Title: 量子噪声层析成像与物理信息神经网络

Abstract: Characterizing the environmental interactions of quantum systems is a critical bottleneck in the development of robust quantum technologies. Traditional tomographic methods are often data-intensive and struggle with scalability. In this work, we introduce a novel framework for performing Lindblad tomography using Physics-Informed Neural Networks (PINNs). By embedding the Lindblad master equation directly into the neural network's loss function, our approach simultaneously learns the quantum state's evolution and infers the underlying dissipation parameters from sparse, time-series measurement data. Our results show that PINNs can reconstruct both the system dynamics and the functional form of unknown noise parameters, presenting a sample-efficient and scalable solution for quantum device characterization. Ultimately, our method produces a fully-differentiable digital twin of a noisy quantum system by learning its governing master equation. Show Chinese abstract

Abstract: 表征量子系统的环境相互作用是开发稳健量子技术的关键瓶颈。 传统的层析方法通常数据密集且在可扩展性方面存在困难。 在本工作中，我们引入了一种新颖的框架，使用物理信息神经网络（PINNs）进行林德布洛特定理层析。 通过将林德布洛德主方程直接嵌入神经网络的损失函数中，我们的方法同时学习量子态的演化，并从稀疏的时间序列测量数据中推断出潜在的耗散参数。 我们的结果表明，PINNs可以重建系统动力学和未知噪声参数的功能形式，为量子设备表征提供了一种样本高效且可扩展的解决方案。 最终，我们的方法通过学习其控制主方程，生成了一个完全可微的嘈杂量子系统的数字孪生。