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CRC Press eBooks

Quantification of Quantum Entanglement

M. S. Ramkarthik, Pranay Barkataki

Published Sep 22, 2022• 0 citations
Publisher
DOI
10.1201/9781003285489-5

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Quantum entanglement is one of the essences of quantum mechanics and quantum information theory. It is a physical phenomenon in which entangled particles remain correlated with each other regardless of the distance between them. Quantum entanglement plays a significant role in areas such as quantum computing, quantum cryptography, and quantum teleportation. Quantifying entanglement is important for determining the depth of the entanglement level and has an impact on quantum information tasks performance. Entanglement classification is critical in quantum information theory for determining the class of states in a quantum system. The entanglement classification of two qubits as separable or entangled has been established. The classification of multiqubit entanglement is more challenging, especially in higher-qubit systems. The goal of this study is to identify different established measurements for entanglement quantification and entanglement classification methods through a systematic literature review. Indexed articles between 2017 and 2021 were selected as secondary resources from several sources based on specific keywords. This study presents a conceptual framework of entanglement quantification and classification based on previous studies.

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Quantum entanglement, a fundamental concept in quantum physics, has been elucidated through the development of Bell’s inequalities. Recent advancements have enabled controlled manipulation and measurement of entangled quantum states. This chapter provides a concise overview of entanglement’s mathematical underpinnings, its manipulation, and quantification. Special attention is given to quantification methods and their implications in quantum information. Key principles and measures are presented to facilitate a foundational understanding. Readers are encouraged to supplement this overview with comprehensive review articles and primary literature for further insights.
Quantum entanglement is a key resource in quantum computing and quantum information processing tasks. However, its quantification remains a major challenge since it cannot be directly extracted from physical observables. To address this issue, we study a few machine-learning based models to estimate the amount of entanglement in two-qubit as well as three-qubit systems. We use measurement outcomes as the input features and entanglement measures as the training labels. Our models predict entanglement without requiring the full state information. This demonstrates the potential of machine learning as an effcient and powerful tool for characterizing quantum entanglement
Quantum entanglement is one of the essences of quantum mechanics and quantum information theory. It is a physical phenomenon in which entangled particles remain correlated with each other regardless of the distance between them. Quantum entanglement plays a significant role in areas such as quantum computing, quantum cryptography, and quantum teleportation. Quantifying entanglement is important for determining the depth of the entanglement level and has an impact on quantum information tasks performance. Entanglement classification is critical in quantum information theory for determining the class of states in a quantum system. The entanglement classification of two qubits as separable or entangled has been established. The classification of multiqubit entanglement is more challenging, especially in higher-qubit systems. The goal of this study is to identify different established measurements for entanglement quantification and entanglement classification methods through a systematic literature review. Indexed articles between 2017 and 2021 were selected as secondary resources from several sources based on specific keywords. This study presents a conceptual framework of entanglement quantification and classification based on previous studies.