Real-Time AI Decision Making in IoT with Quantum Computing: Investigating & Exploring the Development and Implementation of Quantum-Supported AI Inference Systems for IoT Applications
- Mohan Raparthi
, - Venkata Siva Prakash Nimmagadda ,
- Mohit Kumar Sahu ,
- Swaroop Reddy Gayam ,
- Sandeep Pushyamitra Pattyam ,
- Krishna Kanth Kondapaka ,
- Bhavani Prasad Kasaraneni ,
- Praveen Thuniki ,
- Siva Sarana Kuna ,
- Sudharshan Putha
Abstract
The Internet of Things (IoT) has revolutionized the way devices communicate and interact, generating vast amounts of data that require real-time processing and decision-making capabilities. Traditional AI systems face challenges in meeting the real-time demands of IoT applications due to computational complexities. Quantum computing has emerged as a potential solution, offering parallel processing power to accelerate AI inference tasks. This paper investigates the integration of quantum computing into AI systems for real-time decision-making in IoT. We explore the development, challenges, and future prospects of quantum-supported AI inference systems for IoT applications, highlighting the potential benefits and limitations of this approach. Through a comprehensive review of existing literature and case studies, we provide insights into the current state of quantum-supported AI inference systems in IoT and discuss the implications for future research and development in this field.
Keywords
Real-Time, AI Decision Making, IoT, Quantum Computing, Quantum-Supported AI Inference Systems, Development, Implementation, Challenges, Future Prospects
References
- Pargaonkar, Shravan. "A Review of Software Quality Models: A Comprehensive Analysis." Journal of Science & Technology 1.1 (2020): 40-53.
- Cai J, Lu J, Ming Z, Wu J. Quantum computing for real-time big data analysis: Opportunities and challenges. J Netw Comput Appl. 2018 Sep;120:129-142. doi: 10.1016/j.jnca.2018.05.015.
- Pargaonkar, Shravan. "Bridging the Gap: Methodological Insights from Cognitive Science for Enhanced Requirement Gathering." Journal of Science & Technology 1.1 (2020): 61-66.
- Farid A, Shabani A, Song Y, Nishimura H, Awais M, Amin MB, Kim SW. Quantum-enhanced artificial intelligence. arXiv preprint arXiv:2108.05580. 2021 Aug 11.
- Pargaonkar, Shravan. "Future Directions and Concluding Remarks Navigating the Horizon of Software Quality Engineering." Journal of Science & Technology 1.1 (2020): 67-81.
- Flügge A, Ertürk N, Bauer G, Haase J. Quantum computing in the cloud: from applications to hardware. ACM Comput Surv. 2021;54(1):1-33. doi: 10.1145/3425277.
- Pargaonkar, S. (2020). A Review of Software Quality Models: A Comprehensive Analysis. Journal of Science & Technology, 1(1), 40-53.
- Hendricks RK, LeCun Y. Distilling knowledge into neural networks. arXiv preprint arXiv:1503.02531. 2015 Mar 9.
- Pargaonkar, S. (2020). Bridging the Gap: Methodological Insights from Cognitive Science for Enhanced Requirement Gathering. Journal of Science & Technology, 1(1), 61-66.
- Jacobson P, Topol EJ. Blockchain Technology in Health Care: A Primer for Surgeons. Ann Surg. 2019 May;269(5):78-81. doi: 10.1097/SLA.0000000000003071.
- Pargaonkar, S. (2020). Future Directions and Concluding Remarks Navigating the Horizon of Software Quality Engineering. Journal of Science & Technology, 1(1), 67-81.
- Lee J, An S, Jung H, Yoo S, Yoon C. Quantum machine learning for personalized cancer care. npj Precis Oncol. 2021 Mar 15;5(1):1-9. doi: 10.1038/s41698-021-00158-2.