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Hybrid Machine Learning Frameworks: Bridging Quantum and Classical Computing for IoT Advancements
Subject area: Science,Engineering and Technology · Area of research: Internet of Things
Abstract
one of the maximum important use instances for deep?learning is image class. the appearance?of quantum technology has extended studies into quantum neural networks (QNNs). In conventional deep getting to know-based totally picture type, the capabilities of the photograph are extracted using a convolutional neural community (CNN)?and choice barriers are defined the usage of a multi-layer perceptron (MLP) network. Conversely, parameterized quantum?circuits can generate complex boundaries on selections and extract rich capabilities from images. This study proposed a hybrid QNN (H-QNN) model in binary picture class scenario to advantage from each QNN and?quantum computing. Our H-QNN model is distinctly efficient for computation on nosier intermediate-scale quantum (NISQ) devices, which are the?front-give up for quantum computing packages nowadays. this is accomplished by way of using a tensor product country of a small, -qubit quantum circuit to?be paired with a classical convolutional architecture. The?proposed H-QNN version can achieve 90.1% accuracy on binary image datasets, which substantially improves the classification accuracy. greater importantly, the proposed H-QNN and?the baseline CNN models are substantially evaluated at the image retrieval tasks as nicely. Quantitative consequences received show the generalisation of our H-QNN?for the downstream image retrieval tasks. by means of addressing the overfitting hassle for small datasets, our version is a valuable resource?for real-world applications.
Keywords
Quantum Convolutional Neural Networks, Hybrid Quantum?Classical Neural Networks, Image Retrieval, Classification, and Quantum Machine Learning.
References
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[31] Shakir, Khan, and Alotaibi Reemiah Muneer. "A novel thresholding for prediction analytics with machine learning techniques." International Journal of Computer Science & Network Security 23.1 (2023): 33-40.
[32] Tayyab, Moeen, et al. "Recognition of Visual Arabic Scripting News Ticker From Broadcast Stream." IEEE Access 10 (2022): 59189-59204.
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[43] Khan, Shakir, et al. "Keeping Data on Clouds: Cloud Computing Significance." International Journal of Engineering & Science Research 3.2 (2013): 2321-2327.
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[49] Khan, Shakir, and Arun Sharma. "Moodle Based LMS and Open Source Software (OSS) Efficiency in E-Learning." International Journal of Computer Science & Engineering Technology 3.4 (2012): 50-60.
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[51] AlAjmi, Mohamed F., Shakir Khan, and Arun Sharma. "Studying data mining and data warehousing with different e-learning system." International Journal of Advanced Computer Science and Applications 4.1 (2013).
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[57] Jian, Wang, et al. "SA-Bi-LSTM: Self Attention With Bi-Directional LSTM based Intelligent Model for Accurate Fake News Detection to ensured information integrity on social media platforms." IEEE Access (2024).
[58] S. Khan and S. Alqahtani, "Hybrid machine learning models to detect signs of depression," Multimedia Tools and Applications, pp. 1-19, 2023.
[59] Eldosoky, Mahmoud A., Jian Ping Li, Amin Ul Haq, Fanyu Zeng, Mao Xu, Shakir Khan, and Inayat Khan. "WallNet: Hierarchical Visual Attention-Based Model for Putty Bulge Terminal Points Detection." The Visual Computer (2024): 1-16.
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[62] Sreekumar, Das, S., Debata, B.R., Gopalan, R., Khan, S. (2024). Diabetes Prediction: A Comparison Between Generalized Linear Model and Machine Learning. In: Acharjya, D.P., Ma, K. (eds) Computational Intelligence in Healthcare Informatics. Studies in Computational Intelligence, vol 1132. Springer, Singapore. https://doi.org/10.1007/978-981-99-8853-2_4
[63] Khan, S., Serajuddin, M., Hasan, Z., Alvi, S.A.M., Ayub, R., Sharma, A. (2025). Natural Language Generation (NLG) with Reinforcement Learning (RL). In: Dev, A., Sharma, A., Agrawal, S.S., Rani, R. (eds) Artificial Intelligence and Speech Technology. AIST 2023. Communications in Computer and Information Science, vol 2268. Springer, Cham. https://doi.org/10.1007/978-3-031-75167-7_25
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[65] Khan, S., Khari, M. & Azrour, M. IoT in retail and e-commerce. Electron Commer Res (2023). https://doi.org/10.1007/s10660-023-09785-3
[66] Halder, P., Hassan, M.M., Rahman, A.K.Z.R., Akter, L., Ahmed, A.S., Khan, S., Chatterjee, S., Raihan, M.: Prospects and setbacks for migrating towards 5G wireless access in developing Bangladesh: A comparative study. J. Eng. 2023, e12319 (2023). https://doi.org/10.1049/tje2.12319
[67] S. Khan et al., "Manufacturing industry based on dynamic soft sensors in integrated with feature representation and classification using fuzzy logic and deep learning architecture," The International Journal of Advanced Manufacturing Technology, vol. 128, pp. 2885–2897, 2023.
[68] Alotaibi, Reemiah Muneer, and Shakir Khan. "Big Data and Predictive Data Analytics in the Smes Industry Using Machine Learning Approach." 2023 6th International Conference on Contemporary Computing and Informatics (IC3I). Vol. 6. IEEE, 2023.
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[79] AlSuwaidan, Lulwah, et al. "Swarm Intelligence Algorithms for Optimal Scheduling for Cloud‐Based Fuzzy Systems." Mathematical Problems in Engineering 2022.1 (2022): 4255835.
[80] Sultan Ahmad, Sudan Jha, Abubaker E. M. Eljialy and Shakir Khan, “A Systematic Review on e-Wastage Frameworks” International Journal of Advanced Computer Science and Applications (IJACSA), 12(12), 2021.
[81] Khan, Shakir. "Visual Data Analysis and Simulation Prediction for COVID-19 in Saudi Arabia Using SEIR Prediction Model." International Journal of Online & Biomedical Engineering 17.8 (2021).
[82] Khan, Shakir, and Mohammed Altayar. "Industrial internet of things: Investigation of the applications, issues, and challenges." Int. J. Adv. Appl. Sci 8.1 (2021): 104-113.
[83] S. Khan, "Study Factors for Student Performance Applying Data Mining Regression Model Approach," International Journal of Computer Science Network Security, vol. 21, no. 2, pp. 188-192, 2021.
[84] Khan, Shakir, and Amani Alfaifi. "Modeling of coronavirus behavior to predict it’s spread." International Journal of Advanced Computer Science and Applications 11.5 (2020): 394-399.
[85] S. Khan and M. Alshara, "Development of Arabic evaluations in information retrieval," International Journal of Advanced Applied Sciences, vol. 6, no. 12, pp. 92-98, 2019.
[86] S. Khan and M. Alshara, "Fuzzy Data Mining Utilization to Classify Kids with Autism," International Journal of Computer Science Network Security, vol. 19, no. 2, pp. 147-154, 2019.
[87] S. Khan and M. F. AlAjmi, "A Review on Security Concerns in Cloud Computing and their Solutions," International Journal of Computer Science Network Security, vol. 19, no. 2, p. 10, 2019.
[88] Khan, Shakir. "Modern Internet of Things as a challenge for higher education." International Journal of Computer Science and Network Security 18.12 (2018): 34-41.
[89] S. Khan, A. S. Al-Mogren, and M. F. AlAjmi, "Using cloud computing to improve network operations and management," presented at the 5th National Symposium on Information Technology: Towards New Smart World (NSITNSW), 2015.
[90] AlAjmi, Mohamed F., and Shakir Khan. "Effective Use of Web 2.0 Tools Complex Pharmatical Skills Teaching And Learning." ICERI2011, 3rd International Conference on Education and New Learning Technologies, Spain. 2011.
[91] M. F. AlAjmi, S. Khan, and A. Sharma, "Collaborative learning outline for mobile environment," in 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), 2014, pp. 429-434: IEEE.
[92] S. Khan, P. Sharma, K. R. Prasad, S. D, M. Serajuddin and R. Ayub, "The Implementation of Machine Learning in the Development of Sustainable Supply Chains," 2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), Gautam Buddha Nagar, India, 2023, pp. 292-296, doi: 10.1109/UPCON59197.2023.10434528.
[93] Tayyab, Moeen, et al. "Recognition of Visual Arabic Scripting News Ticker From Broadcast Stream." IEEE Access 10 (2022): 59189-59204.
[94] Khan, Shakir. "Business Intelligence Aspect for Emotions and Sentiments Analysis." 2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT). IEEE, 2022.
[95] Khan, Shakir, and Mohammed Ali Alshara. "Adopting Open Source Software for Integrated Library System and Digital Library Automation." International Journal of Computer Science and Network Security 20.9 (2020): 158-165.
[96] Khan, Shakir, and M. Alajmi. "The Role Of Open Source Technology In Development Of E-Learning Education." Edulearn17 Proceedings. IATED, 2017.
[97] AlAjmi, M., and Shakir Khan. "Part of Ajax And Openajax In Cutting Edge Rich Application Advancement For E-Learning." INTED2015 Proceedings. IATED, 2015.
[98] Sattar, Kamran, et al. "Social networking in medical schools: Medical student’s viewpoint." Biomed Res 27.4 (2016): 1378-84.
[99] AlAjmi, Mohamed F., Shakir Khan, and Abdulkadir Alaydarous. "Data Protection Control and Learning Conducted Via Electronic Media IE Internet." International Journal of Advanced Computer Science and Applications 5.11 (2014).
[100] Khan, Shakir, et al. "Keeping Data on Clouds: Cloud Computing Significance." International Journal of Engineering & Science Research 3.2 (2013): 2321-2327.
[101] AlAjmi, Mohammed, and Shakir Khan. "Data Mining–Based, Service Oriented Architecture (SOA) In E-Learning." Iceri2012 Proceedings. IATED, 2012.
[102] AlAjmi, M., and Shakir Khan. "The Utility of New Technologies in Enhancing Learning Vigilance in Educationally Poor Populations." EDULEARN12 Proceedings. IATED, 2012.
[103] Alajmi, M., and S. Khan. "EFFECTIVE USE OF WEB 2.0 TOOLS IN PHARMACY STUDENTS'CLINICAL SKILLS PRACTICE DURING FIELD TRAINING." iceri2011 proceedings. IATED, 2011.
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How to cite this paper
@article{1707330,
author = {Amjad Khan, Ashish Kumar Pandey},
title = {Hybrid Machine Learning Frameworks: Bridging Quantum and Classical Computing for IoT Advancements},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {10},
pages = {425-436},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1707330.pdf},
abstract = {one of the maximum important use instances for deep?learning is image class. the appearance?of quantum technology has extended studies into quantum neural networks (QNNs). In conventional deep getting to know-based totally picture type, the capabilities of the photograph are extracted using a convolutional neural community (CNN)?and choice barriers are defined the usage of a multi-layer perceptron (MLP) network. Conversely, parameterized quantum?circuits can generate complex boundaries on selections and extract rich capabilities from images. This study proposed a hybrid QNN (H-QNN) model in binary picture class scenario to advantage from each QNN and?quantum computing. Our H-QNN model is distinctly efficient for computation on nosier intermediate-scale quantum (NISQ) devices, which are the?front-give up for quantum computing packages nowadays. this is accomplished by way of using a tensor product country of a small, -qubit quantum circuit to?be paired with a classical convolutional architecture. The?proposed H-QNN version can achieve 90.1% accuracy on binary image datasets, which substantially improves the classification accuracy. greater importantly, the proposed H-QNN and?the baseline CNN models are substantially evaluated at the image retrieval tasks as nicely. Quantitative consequences received show the generalisation of our H-QNN?for the downstream image retrieval tasks. by means of addressing the overfitting hassle for small datasets, our version is a valuable resource?for real-world applications.},
keywords = {Quantum Convolutional Neural Networks, Hybrid Quantum?Classical Neural Networks, Image Retrieval, Classification, and Quantum Machine Learning.},
month = {April},
}