Quantum State Tomography using Quantum Machine Learning

TL;DR

This paper introduces a quantum machine learning approach to quantum state tomography that significantly reduces measurement requirements while maintaining high fidelity, advancing practical quantum information processing.

Contribution

It presents a novel integration of quantum machine learning techniques into quantum state tomography, improving efficiency over traditional methods.

Findings

Achieves 98% fidelity in quantum state reconstruction

Requires fewer measurements than conventional QST methods

Effective on both simulated and experimental multi-qubit systems

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.