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Exploring the String Theory Landscape Using Quantum Machine Learning: A Novel Theoretical Framework

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<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.

Description

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>

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Catalogue records · 1

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Provenance · 1 source records, 12 field assertions
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ZivaHuboai:figshare.com:article/340235645 d agoJSON v1
FieldAssertionExtractorEvidence
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concepts[field].anzsrc:field:490203mapping · zivahub uct ac zavocabulary-mapper@1.0.0keywords['Mathematical aspects of classical mechanics, quantum mechanics and quantum information theory']
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concepts[field].anzsrc:field:510702mapping · zivahub uct ac zavocabulary-mapper@1.0.0keywords['string Theory']
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