用物理神经网络高效重建量子系统噪声，仅需少量数据即可完成精准建模。
Quantum Noise Tomography with Physics-Informed Neural Networks
- 将林德布拉德主方程嵌入神经网络损失函数，实现动力学与噪声参数联合学习。
- 在稀疏时间序列数据下，准确恢复系统演化轨迹和未知噪声形式。
- 适合需要高效率量子设备表征的科研与工程人员，尤其适用于数据受限场景。
量子系统环境相互作用的表征是发展鲁棒量子技术的关键瓶颈。传统层析方法通常数据需求量大且难以扩展。本文提出一种基于物理信息神经网络（PINNs）的林德布拉德层析新框架。通过将林德布拉德主方程直接嵌入神经网络的损失函数，该方法可从稀疏的时间序列测量数据中，同时学习量子态演化并推断底层耗散参数。结果表明，PINNs能重建系统动力学及未知噪声参数的函数形式，提供了一种样本高效且可扩展的量子器件表征方案。最终，该方法通过学习系统的控制主方程，生成了一个完全可微的噪声量子系统数字孪生体。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。