Quantum-Inspired Neural Networks: Accelerating AI for Large-Scale Data Processing

Year : 2025 | Volume : 12 | Issue : 03 | Page : 12 17
    By

    Suruchi Shirole,

  • Yashswini Ambale,

  1. Student, Department of Computer Engineering, Rajgad Dnyanpeeth’s Technical Campus Polytechnic, Gate no. 237, Pune Bangalore Highway, Dhangawadi, Tal. Bhor, Dist. Pune, Maharashtra, India
  2. Student, Department of Computer Engineering, Rajgad Dnyanpeeth’s Technical Campus Polytechnic, Gate no. 237, Pune Bangalore Highway, Dhangawadi, Tal. Bhor, Dist. Pune, Maharashtra, India

Abstract

Recently, the world of artificial intelligence has been buzzing with exciting ideas inspired by quantum computing, especially when it comes to processing large amounts of data. Introducing the Quantum-Inspired Neural Network (QINN), a novel approach to conventional neural networks that blends concepts from quantum mechanics with machine learning techniques. Unlike typical networks that rely on neurons, QINNs utilize qubit-based representations, enabling them to perform computations in a more flexible and powerful way. By applying gradient descent techniques drawn from quantum mechanics, these networks not only learn more effectively but also simplify the computational load. Early experiments show that QINNs can achieve quicker convergence and greater accuracy than conventional models, making them particularly promising for big data tasks. This research opens the door to a new era where quantum ideas can enhance AI, paving the way for future explorations into hybrid systems that blend the best of both worlds.

Keywords: Artificial intelligence, quantum computing, quantum-inspired neural network (QINN), neural networks, quantum mechanics, machine learning, qubit-based representations, gradient descent, convergence, accuracy, big data, hybrid systems

[This article belongs to Journal of Software Engineering Tools & Technology Trends ]

How to cite this article:
Suruchi Shirole, Yashswini Ambale. Quantum-Inspired Neural Networks: Accelerating AI for Large-Scale Data Processing. Journal of Software Engineering Tools & Technology Trends. 2025; 12(03):12-17.
How to cite this URL:
Suruchi Shirole, Yashswini Ambale. Quantum-Inspired Neural Networks: Accelerating AI for Large-Scale Data Processing. Journal of Software Engineering Tools & Technology Trends. 2025; 12(03):12-17. Available from: https://journals.stmjournals.com/josettt/article=2025/view=227089


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Regular Issue Subscription Review Article
Volume 12
Issue 03
Received 10/04/2025
Accepted 04/05/2025
Published 15/09/2025
Publication Time 158 Days
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