Exploring the Synergy Between Neuro-Inspired Algorithms and Quantum Computing in Machine Learning
DOI:
https://doi.org/10.22399/ijcesen.2484Keywords:
Neuro-inspired algorithms, Quantum computing, Quantum neural networks, Machine learning, Deep learning, Quantum optimizationAbstract
The integration of neuro-inspired algorithms and quantum computing in machine learning presents a promising frontier for addressing complex computational challenges in modern AI. Neuro-inspired algorithms, such as artificial neural networks (ANNs), deep learning (DL), and spiking neural networks (SNNs), have demonstrated impressive performance in various domains, including image recognition, natural language processing, and autonomous systems. This research explores the synergy between neuro-inspired algorithms and quantum computing, focusing on how quantum-enhanced machine learning models can accelerate training and inference processes in neuro-inspired systems. Quantum neural networks (QNNs) leverage quantum principles, such as superposition and entanglement, to represent and manipulate data in ways that classical systems cannot. By combining quantum computing's parallelism with the flexibility and learning capability of neuro-inspired algorithms, the proposed hybrid models can provide exponential speedups in tasks involving large-scale data processing and optimization. To evaluate the performance of these hybrid models, experiments were conducted using a quantum-enhanced deep learning model applied to image classification and a neuro-inspired algorithm augmented by quantum optimization techniques for optimization tasks. The quantum-enhanced deep learning model achieved a 45% reduction in training time compared to classical deep learning models while maintaining similar accuracy levels. These findings highlight the significant potential of combining quantum computing with neuro-inspired algorithms, opening new avenues for faster, more efficient machine learning models capable of solving previously unsolvable problems. The synergy between these two domains could lead to breakthroughs in areas like artificial general intelligence (AGI), drug discovery, and autonomous systems, where large-scale optimization and pattern recognition are critical.
References
[1] Feynman, R. P. (1981). Simulating physics with computers. International Journal of Theoretical Physics, 21(6), 467-488.
Doi: 10.1007/BF02650179
[2] Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504-507.
Doi: 10.1126/science.1127647
[3] Lloyd, S., Mohseni, M., & Rebentrost, P. (2013). Quantum algorithms for fixed qubit architectures. Nature Physics, 10, 631-635.
Doi: 10.1038/nphys3029
[4] Sood, K., Dhanaraj, R.K., Balusamy, B., Grima, S. and Uma Maheshwari, (2022). Prelims. R. (Ed.) Big Data: A Game Changer for Insurance Industry (Emerald Studies in Finance, Insurance, and Risk Management), Emerald Publishing Limited, Leeds, i-xxiii. https://doi.org/10.1108/978-1-80262-605-620221020
[5] Janarthanan, R.; Maheshwari, R.U.; Shukla, P.K.; Shukla, P.K.; Mirjalili, S.; Kumar, M. (2021), Intelligent Detection of the PV Faults Based on Artificial Neural Network and Type 2 Fuzzy Systems. Energies, 14, 6584. https://doi.org/10.3390/en14206584
[6] Maheshwari, R.U., Kumarganesh, S., K V M, S. et al. (2024), Advanced Plasmonic Resonance-enhanced Biosensor for Comprehensive Real-time Detection and Analysis of Deepfake Content. Plasmonics https://doi.org/10.1007/s11468-024-02407-0 .
[7] McClean, J. R., et al. (2016). The theory of variational hybrid quantum-classical algorithms. New Journal of Physics, 18(2), 023023. Doi: 10.1088/1367-2630/18/2/023023
[8] Farhi, E., & Neven, H. (2018). Classification with quantum neural networks on near term processors. arXiv preprint arXiv:1802.06002.
https://doi.org/10.48550/arXiv.1802.06002
[9] Benedetti, M., et al. (2019). Quantum-assisted learning of quantum data. Quantum, 3, 198.
Doi: 10.22331/q-2019-12-23-198
[10] Shimamura, S., et al. (2020). Quantum-inspired deep learning models and their applications. Nature Communications, 11, 3025.
Doi: 10.1038/s41467-020-16871-0
[11] Huang, H. Y., et al. (2020). Power of data in quantum machine learning. Nature Communications, 11(1), 1000.
Doi: 10.1038/s41467-020-14852-5
[12] Biamonte, J., & Woerner, S. (2020). Quantum machine learning and artificial intelligence: A review. Quantum Science and Technology, 5(4), 043002.
Doi: 10.1088/2058-9565/ab8f59
[13] Cai, J., et al. (2019). Quantum-inspired optimization algorithm based on hybrid neural networks. Nature Scientific Reports, 9(1), 11495.
Doi: 10.1038/s41598-019-48024-0
[14] Zhou, X., et al. (2021). Quantum neural networks: Advances and challenges. npj Quantum Information, 7(1), 16.
Doi: 10.1038/s41534-021-00370-4
[15] Zhou, L., & Li, S. (2020). Exploring quantum-inspired algorithms in deep learning models. IEEE Transactions on Neural Networks and Learning Systems, 31(5), 1713-1725.
Doi: 10.1109/TNNLS.2019.2942098
[16] Kiani, S., et al. (2021). Quantum-enhanced reinforcement learning for robotic control. IEEE Transactions on Neural Networks and Learning Systems, 32(7), 2879-2890.
Doi: 10.1109/TNNLS.2020.2972598
[17] Carrasquilla, J., et al. (2017). Machine learning for quantum phase recognition. Scientific Reports, 7, 41125.
Doi: 10.1038/srep41125
[18] Gupta, A., et al. (2021). Towards hybrid quantum-classical algorithms for machine learning: A review. Journal of Machine Learning Research, 22, 1-35.
[19] S. Ramalingam, P. Gowsikraja, E. Loganathan, A. Logeswaran, N. Ramkumar and K. Geetha, (2024), Attention-Based RNN for Alzheimer's Diagnosis: A Multi-Modal Approach with Enhanced Feature Retention Algorithm, First International Conference for Women in Computing (InCoWoCo), Pune, India, 2024, 1-5, doi: 10.1109/InCoWoCo64194.2024.10863383.
[20] Anandakumar, H., Umamaheswari, K. (2017), Supervised machine learning techniques in cognitive radio networks during cooperative spectrum handovers. Cluster Comput 20, 1505–1515. https://doi.org/10.1007/s10586-017-0798-3
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
