AI for Quantum Error Correction

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@article{1707534,
      author = {Atharv Atmaram Jadhav, Prof. Dhanashri A. Gore, Prof. Neeta Dimbale},
      title = {AI for Quantum Error Correction},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {9},
      pages = {736-741},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707534.pdf},
      abstract = {Quantum computing holds immense promise for solving complex problems beyond the capabilities of classical systems. However, practical implementation faces significant challenges, primarily due to the inherent fragility of quantum states and susceptibility to noise. Quantum error correction (QEC) is a vital component for realizing fault-tolerant quantum computation. Artificial intelligence (AI) techniques, particularly machine learning (ML) and deep learning (DL), have emerged as powerful tools to enhance QEC by optimizing error detection, correction, and noise mitigation. This paper explores the intersection of AI and QEC, presenting recent advancements, methodologies, and future directions for integrating AI into quantum error correction frameworks.},
      keywords = {Quantum Computing, Quantum Error Correction (QEC), Fault-Tolerant Computing, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Quantum Noise, Noise Mitigation, Error Detection, Quantum Algorithms, Quantum Circuits, Quantum Hardware, QEC Codes, Adaptive Learning, Quantum Error Syndrome, Quantum State Fragility, AI in QEC, Quantum Fault Tolerance, Quantum Software Optimization, Quantum Information Processing},
      month = {March},
  }