Quantum neural networks to detect entanglement transitions in quantum many-body systems

Quantum entanglement becomes increasingly complex to analyze in many-body systems due to exponential growth in complexity with system size. In this work, we explore the potential of quantum machine learning (QML) to circumvent this. Specifically, we train a parameterized quantum neural network (QNN)...

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Bibliographic Details
Published in:Physica scripta 2025-01, Vol.100 (1)
Main Authors: El Ayachi, F, Ait Mansour, H, El Baz, M
Format: Article
Language:English
Subjects:
entanglement
quantum
quantum neural network
transitions
Online Access:Get full text
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Summary:Quantum entanglement becomes increasingly complex to analyze in many-body systems due to exponential growth in complexity with system size. In this work, we explore the potential of quantum machine learning (QML) to circumvent this. Specifically, we train a parameterized quantum neural network (QNN) model to detect transitions in the entanglement properties of the ground state in a multi-spin Ising model. This approach enables the classification of different entanglement states and provides deeper insights into the behavior of entanglement under multi-spin interactions. Our results demonstrate that QML can effectively simplify the classification process and overcome the complexity challenges encountered by classical algorithms.
ISSN:0031-8949
1402-4896
DOI:10.1088/1402-4896/ad9422