Exploring the Phase Diagram of the quantum one-dimensional ANNNI model

Abstract: In this manuscript, we explore the intersection of Quantum Machine Learning (QML) and Tensor Networks (TNs) in the context of the one-dimensional Axial Next-Nearest-Neighbour Ising (ANNNI) model with a transverse field. The study aims to concretely connect QML and TN by combining them in various stages of algorithm construction, focusing on phase diagram reconstruction for the ANNNI model, with supervised and unsupervised techniques. The model’s significance lies in its representation of quantum fluctuations and frustrated exchange interactions, making it a paradigm for studying magnetic ordering, frustration, and the presence of a floating phase. It concludes with discussions of the results, including insights from increased system sizes and considerations for future work, such as addressing limitations in Quantum Convolutional Neural Networks (QCNNs) and exploring more realistic implementations of Quantum Circuits (QCs).

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Recommended citation:

@article{cea2024exploring,
   title={Exploring the Phase Diagram of the quantum one-dimensional ANNNI model},
   author={Maria, Cea and Michele, Grossi and Saverio, Monaco and Enrique, Rico and Luca, Tagliacozzo and Sofia, Vallecorsa}
   journal={ArXiv Preprint},
   url={https://arxiv.org/abs/2402.11022}
   year={2024}}