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Cognitive Computing and Cyber Physical Systems. 5th EAI International Conference, IC4S 2024, Bhimavaram, India, April 5–7, 2024, Proceedings, Part-I

Research Article

Investigation of Quantum Machine Learning for Smart Eco System Focusing on Energy Optimization

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BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-77075-3_12,
        author={S. Mahaboob Hussain and Nishit Malviya and Prakash Pareek},
        title={Investigation of Quantum Machine Learning for Smart Eco System Focusing on Energy Optimization},
        proceedings={Cognitive Computing and Cyber Physical Systems. 5th EAI International Conference, IC4S 2024, Bhimavaram, India, April 5--7, 2024, Proceedings, Part-I},
        proceedings_a={IC4S},
        year={2025},
        month={2},
        keywords={Quantum Machine Learning Smart Cities Urban Development Optimization Sustainability Data Privacy Security Energy Optimization Quantum Variational Algorithm},
        doi={10.1007/978-3-031-77075-3_12}
    }
    
  • S. Mahaboob Hussain
    Nishit Malviya
    Prakash Pareek
    Year: 2025
    Investigation of Quantum Machine Learning for Smart Eco System Focusing on Energy Optimization
    IC4S
    Springer
    DOI: 10.1007/978-3-031-77075-3_12
S. Mahaboob Hussain1, Nishit Malviya2,*, Prakash Pareek1
  • 1: Vishnu Institute of Technology
  • 2: Indian Institute of Information Technology
*Contact email: nishit.malviya@iiitranchi.ac.in

Abstract

This work explores the integration of Quantum Machine Learning (QML) with various applications in development of smart eco system, focusing on its potential to optimize various urban systems and address complex challenges. Mainly, we explored energy management and optimization techniques, considering Quantum Variational Algorithm (QVL). With this study we recognize that the integration of quantum machine learning approaches in smart city applications enhance the sustainability goals with comparison to the classical techniques. Further, we also investigated on how QML algorithms can revolutionize in various aspects in easing transportation, digital public services and policies, security enhancements, and urban planning, offering opportunities to enhance efficiency, sustainability, and resilience in urban environments. However, this study presents several challenges, including scalability limitations, data privacy concerns, and security vulnerabilities in smart cities. Application of QML holds immense promise for urban innovation and transformation. Discussed, various future directions for integrating QML in smart cities applications.

Keywords
Quantum Machine Learning Smart Cities Urban Development Optimization Sustainability Data Privacy Security Energy Optimization Quantum Variational Algorithm
Published
2025-02-09
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-77075-3_12
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