AI for Quantum Error Correction

• Author(s): Atharv Atmaram Jadhav ; Prof. Dhanashri A. Gore ; Prof. Neeta Dimbale
• Paper ID: 1707534
• Page: 736-741
• Published Date: 18-03-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 9 March-2025

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

Citations

IRE Journals:
Atharv Atmaram Jadhav , Prof. Dhanashri A. Gore , Prof. Neeta Dimbale "AI for Quantum Error Correction" Iconic Research And Engineering Journals Volume 8 Issue 9 2025 Page 736-741

IEEE:
Atharv Atmaram Jadhav , Prof. Dhanashri A. Gore , Prof. Neeta Dimbale "AI for Quantum Error Correction" Iconic Research And Engineering Journals, 8(9)