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

TL;DR

This paper investigates the phase diagram of the one-dimensional ANNNI model using quantum machine learning and tensor networks, aiming to improve understanding of quantum fluctuations and frustrated interactions.

Contribution

It introduces a novel integration of QML and tensor networks for phase diagram reconstruction in the ANNNI model, advancing methods for analyzing complex quantum systems.

Findings

Successful phase diagram reconstruction with increased system sizes

Insights into quantum fluctuations and frustration effects

Discussion of limitations and future directions for QCNN and quantum computing

Abstract

In this manuscript, we explore the intersection of QML and TN in the context of the one-dimensional 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 QCNN and exploring more realistic implementations of QC.