Overview of Quantum Computing in Quantum Neural Network and Artificial Intelligence

Authors

  • M. Vasuki Associate Professor, Department of Master Computer Application, Sri Manakula Vinayagar Engineering College, Pondicherry, IND. Author
  • Dr. A. Karunamurthy Associate Professor, Department of Master Computer Application, Sri Manakula Vinayagar Engineering College, Pondicherry, IND. Author
  • R. Ramakrishnan Associate Professor, Department of Master Computer Application, Sri Manakula Vinayagar Engineering College, Pondicherry, IND. Author
  • G. Prathiba Student, Department of Master Computer Application, Sri Manakula Vinayagar Engineering College, Pondicherry, IND. Author

DOI:

https://doi.org/10.54368/qijirse.2.2.0013

Keywords:

Artificial Intelligence, Machine Learning, Neural Networks, Quantum Algorithms, Quantum Computing, QNN, Quantum Neural Networks

Abstract

In recent years, quantum computing has emerged as a potentially game-changing technology, with applications across various disciplines, including AI and machine learning. In recent years, the combination of quantum computing and neural networks has led to the development of quantum neural networks (QNNs). This paper explores the potential of QNNs and their applications in solving complex problems that are challenging for classical neural networks. This paper explores the fundamental principles of quantum computing, the architecture of QNNs, and their advantages over classical neural networks. Furthermore, this will highlight key research areas and challenges in the development and utilization of QNNs. Through an in-depth analysis, it demonstrates the QNNs hold significant promise for addressing complex computational problems and advancing the field of artificial intelligence.

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Published

2024/08/10

Issue

Section

Original Articles

How to Cite

Vasuki, M., Karunamurthy, A., Ramakrishnan, R., & Prathiba, G. (2024). Overview of Quantum Computing in Quantum Neural Network and Artificial Intelligence. Quing: International Journal of Innovative Research in Science and Engineering, 2(2), 117-127. https://doi.org/10.54368/qijirse.2.2.0013