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October 1, 2025International Journal of Engineering Science and Information Technology

Machine Learning-guided Synthesis of Quantum Entangled Materials

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Authors

PVPriya VijMNMalay K. NandyMPMamta Pandey

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Overview

The study reveals improved synthesis efficiency and predictive accuracy in quantum materials, highlighting the roles of machine learning and reinforcement learning.

Key Points

  • The proposed method significantly enhances entanglement fidelity in quantum materials.
  • Graph neural networks extract quantum features while generative models innovate novel structures for synthesis.
  • Utilizing reinforcement learning reduces experimental failures and increases reproduction rates for quantum materials.
  • This hybrid framework is scalable, enabling various applications in quantum technologies like cryptography and nanomaterials.

Cite This Study

Vij et al. (2025) studied this question.

synapsesocial.com/papers/68dd91c7fe798ba2fc49849dhttps://doi.org/10.52088/ijesty.v5i2.1496
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