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

[{“box”:0,”content”:”n[if 992 equals=”Open Access”]n

n

n

n

Open Access

nn

n

n[/if 992]n[if 2704 equals=”Yes”]n

n

Notice

nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

n[/if 2704]n

n

Year : 2025 [if 2224 equals=””]15/09/2025 at 11:33 AM[/if 2224] | [if 1553 equals=””] Volume : 12 [else] Volume : 12[/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03 | Page : 12 17

n

n

nn

n

n

n

    By

    n

    [foreach 286]n

    n

    Suruchi Shirole, Yashswini Ambale,

    n t

  • n

    n[/foreach]

    n

n[if 2099 not_equal=”Yes”]n

    [foreach 286] [if 1175 not_equal=””]n t

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

    2. n[/if 1175][/foreach]

n[/if 2099][if 2099 equals=”Yes”][/if 2099]n

n

Abstract

n

n

nRecently, 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.nn

n

n

n

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

n[if 424 equals=”Regular Issue”][This article belongs to Journal of Software Engineering Tools & Technology Trends ]

n

[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Journal of Software Engineering Tools & Technology Trends (josettt)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

n

n

n

How to cite this article:
nSuruchi Shirole, Yashswini Ambale. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]Quantum-Inspired Neural Networks: Accelerating AI for Large-Scale Data Processing[/if 2584]. Journal of Software Engineering Tools & Technology Trends. 15/09/2025; 12(03):12-17.

n

How to cite this URL:
nSuruchi Shirole, Yashswini Ambale. [if 2584 equals=”][226 striphtml=1][else]Quantum-Inspired Neural Networks: Accelerating AI for Large-Scale Data Processing[/if 2584]. Journal of Software Engineering Tools & Technology Trends. 15/09/2025; 12(03):12-17. Available from: https://journals.stmjournals.com/josettt/article=15/09/2025/view=0

nn

n

n[if 992 equals=”Open Access”]Full Text PDF[/if 992]n

n

n[if 992 not_equal=”Open Access”]n

n

n[/if 992]n

nn

nnn

n[if 379 not_equal=””]nn

Browse Figures

n

n

n[foreach 379]

figures

[/foreach]n

n

n

n[/if 379]

n

n

n

n

n

References n

n[if 1104 equals=””]n

  1. Kak SC. Quantum neural computing. Adv Imaging Electron Phys. 1995 Jan 1; 94: 259–313.
  2. Chrisley R. Quantum learning. In New directions in cognitive science: Proceedings of the international symposium, Saariselka. 1995 Aug; 4–9.
  3. da Silva AJ, Ludermir TB, de Oliveira WR. Quantum perceptron over a field and neural network architecture selection in a quantum computer. Neural Netw. 2016 Apr 1; 76: 55–64.
  4. Panella M, Martinelli G. Neural networks with quantum architecture and quantum learning. Int J Circuit Theory Appl. 2011 Jan; 39(1): 61–77.
  5. Schuld M, Sinayskiy I, Petruccione F. The quest for a quantum neural network. Quantum Inf Process. 2014 Nov; 13(11): 2567–86.
  6. Beer K, Bondarenko D, Farrelly T, Osborne TJ, Salzmann R, Scheiermann D, Wolf R. Training deep quantum neural networks. Nat Commun. 2020 Feb 10; 11(1): 808.
  7. Wan KH, Dahlsten O, Kristjánsson H, Gardner R, Kim MS. Quantum generalisation of feedforward neural networks. NPJ Quantum Inf. 2017 Sep 14; 3(1): 36.
  8. Peruš M. Neural networks as a basis for quantum associative networks. Neural Netw World. 2000 Oct; 10(6): 1001–13.
  9. Gupta S, Zia RK. Quantum neural networks. J Comput Syst Sci. 2001 Nov 1; 63(3): 355–83.
  10. Gupta S, Zia RK. Quantum neural networks. Journal of Computer and System Sciences. 2001 Nov 1;63(3):355–83.

nn[/if 1104][if 1104 not_equal=””]n

    [foreach 1102]n t

  1. [if 1106 equals=””], [/if 1106][if 1106 not_equal=””],[/if 1106]

    2. n[/foreach]

n[/if 1104]

n

nn[if 1114 equals=”Yes”]n

n[/if 1114]

n

n

[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Review Article

n

n

[if 2146 equals=”Yes”][/if 2146][if 2146 not_equal=”Yes”][/if 2146]n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n[if 1748 not_equal=””]

[else]

[/if 1748]n

n[if 1746 equals=”Retracted”]n

n

n

n

[/if 1746]n[if 4734 not_equal=””]

n

n

n

[/if 4734]n

n

Volume 12
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03
Received 10/04/2025
Accepted 04/05/2025
Published 15/09/2025
Retracted
Publication Time 158 Days

n

n

137

nn

n

Login

n
My IP
n

PlumX Metrics

nn

n

n

n[if 1746 equals=”Retracted”]n

[/if 1746]nnn

nnn”}]