  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.
