Quantum State Tomography (QST) is a fundamental technique in Quantum
Information Processing (QIP) for reconstructing unknown quantum states.
However, the conventional QST methods are limited by the number of measurements
required, which makes them impractical for large-scale quantum systems. To
overcome this challenge, we propose the integration of Quantum Machine Learning
(QML) techniques to enhance the efficiency of QST. In this paper, we conduct a
comprehensive investigation into various approaches for QST, encompassing both
classical and quantum methodologies; We also implement different QML approaches
for QST and demonstrate their effectiveness on various simulated and
experimental quantum systems, including multi-qubit networks. Our results show
that our QML-based QST approach can achieve high fidelity (98%) with
significantly fewer measurements than conventional methods, making it a
promising tool for practical QIP applications.

我们提出将量子机器学习技术与量子态测量 (QST) 相结合，以提高 QST 的效率，通过综合研究了 QST 的多种方法，并实现了不同的量子机器学习方法，并在各种模拟和实验量子系统上展示了其有效性，包括多量子比特网络，结果表明，我们基于量子机器学习的 QST 方法可以以较少的测量次数实现高保真度 (98%)，为实际量子信息处理应用提供了有希望的工具。