Quantum State Tomography using Quantum Machine Learning

Abstract

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.

18 pages, 19 figures

References in corpus (4)

• Experimental Quantum State Tomography of Optical Fields and Ultrafast Statistical Sampling
• Quantum Data Fitting
• Experimental quantum state tomography of a solid state qubit
• A comparative study of estimation methods in quantum tomography

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