Quantum-Inspired Neural Networks for Advanced AI Applications - A Scholarly Review of Quantum Computing Techniques in Neural Network Design

Authors

  • Mohan Raparthi Software Engineer, Google Alphabet (Verily Life Science), Dallas, Texas, USA https://orcid.org/0009-0004-7971-9364
  • Venkata Siva Prakash Nimmagadda Independent Researcher, USA
  • Mohit Kumar Sahu Independent Researcher and Senior Software Engineer, CA, USA
  • Swaroop Reddy Gayam Independent Researcher and Senior Software Engineer at TJMax, USA
  • Sandeep Pushyamitra Pattyam Independent Researcher and Data Engineer, USA
  • Krishna Kanth Kondapaka Independent Researcher, CA ,USA
  • Sudharshan Putha Independent Researcher and Senior Software Developer, USA
  • Praveen Thuniki Independent Research, Sr Program Analyst, Georgia, USA
  • Siva Sarana Kuna Independent Researcher and Software Developer, USA
  • Bhavani Prasad Kasaraneni Independent Researcher, USA

Keywords:

Quantum computing, Quantum-inspired neural networks, Neural network design, Advanced AI applications, Quantum computing techniques

Abstract

Quantum computing has emerged as a promising paradigm for enhancing artificial intelligence (AI) capabilities, particularly in the realm of neural networks. Quantum-inspired neural networks (QINNs) leverage principles from quantum computing to improve the efficiency and performance of traditional neural networks. This paper provides a comprehensive review of QINNs for advanced AI applications, focusing on the integration of quantum computing techniques in neural network design. We discuss the key concepts behind quantum computing, the principles of QINNs, and their potential advantages over classical neural networks. Furthermore, we examine the current state of research in QINNs, highlighting notable advancements and challenges. Through this review, we aim to provide insights into the future prospects of QINNs and their role in shaping the next generation of AI technologies.

References

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Published

20-07-2022

How to Cite

[1]
M. Raparthi, “Quantum-Inspired Neural Networks for Advanced AI Applications - A Scholarly Review of Quantum Computing Techniques in Neural Network Design”, J. Computational Intel. & Robotics, vol. 2, no. 2, pp. 1–8, Jul. 2022.

Issue

Vol. 2 No. 2 (2022): Journal of Computational Intelligence and Robotics

Section

Articles

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