2026-10-06T10:51:03Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34023564 2026-09-29T11:59:10Z category_30094 category_29791 item_type_11 month_year_09_2026

Exploring the String Theory Landscape Using Quantum Machine Learning: A Novel Theoretical Framework Hadis Deldar (21381167) Cosmology and extragalactic astronomy Mathematical aspects of classical mechanics, quantum mechanics and quantum information theory string Theory String Theory & Extra Dimensions <p dir="ltr">This research article proposes a novel theoretical framework that integrates <b>quantum machine learning (QML)</b> techniques into the study of the <b>string theory landscape</b>, aiming to overcome the immense complexity of vacua classification and compactification in string theory. By leveraging variational quantum algorithms, quantum Boltzmann machines, and hybrid quantum-classical architectures, the model explores the possibility of efficient navigation through moduli space and enhanced pattern recognition in Calabi–Yau manifolds. This interdisciplinary approach seeks to contribute a new toolset for theoretical physicists working at the intersection of quantum information science and fundamental high-energy theory.</p> 2026-09-29T11:59:10Z Text Online resource 10.6084/m9.figshare.34023564.v1 https://figshare.com/articles/online_resource/Exploring_the_String_Theory_Landscape_Using_Quantum_Machine_Learning_A_Novel_Theoretical_Framework/34023564 CC BY 4.0