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.

arXiv comment18 pages, 19 figures

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• Gradient-descent methods for fast quantum state tomography
• Comparative Performance Analysis of Quantum Machine Learning Architectures for Credit Card Fraud Detection
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• Quantum noise modeling through Reinforcement Learning
• Robust entanglement measure for mixed quantum states
• Reinforcement learning to learn quantum states for Heisenberg scaling accuracy
• Quantum Bayesian Networks for Machine Learning in Oil-Spill Detection
• Exploring fixed points and eigenstates of quantum systems with reinforcement learning
• Quantum Machine Learning for State Tomography Using Classical Data
• Selective and efficient quantum state tomography for multi-qubit systems