Akademik Veri Yönetim Sistemi
QUANTUM MACHINE INTELLIGENCE, sa. arXiv preprint, ss.1-18, 2024 (ESCI)
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.