Quantum State Tomography using Quantum Machine Learning
This addresses the challenge of making QST practical for large-scale quantum systems in Quantum Information Processing, though it appears incremental as it builds on existing QML techniques.
The paper tackled the problem of reconstructing unknown quantum states in Quantum State Tomography (QST), which is limited by high measurement requirements, and proposed using Quantum Machine Learning (QML) to enhance efficiency, achieving 98% fidelity with significantly fewer measurements.
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.