ItemConference Paper

QiMINT: Quantum-Inspired Mobile Intelligence - Advancing Complex Signal Processing with Machine Learning

Title
QiMINT: Quantum-Inspired Mobile Intelligence - Advancing Complex Signal Processing with Machine Learning
Creator
Kolluru, Vinothkumar; Helen Parimala, E.; Kokilavani, T.; Sunil Raj, Y.; Jesuraj, Bastin; Aminuddin, Afrig
Description
Integrating mobile intelligence with quantum-inspired machine learning (QiML) opens space for challenging mobile signal processing tasks. By utilizing superposition and entanglement, quantum computing principles, QiML boosts the agility of mobile devices by allowing real-time data processing, pattern recognition, and decision making. This work introduces Quantum-Inspired Mobile Intelligence (QiMINT) a new mobile computing framework that integrates quantum-inspired designs with classical machine learning, which increases mobile devices' accuracy, latency, and energy efficiency. Results show that medical QiML based models exceeded traditional machine learning methods in most key performance indicators. The quantum convolutional neural network (QCNN) achieved an accuracy of 92.3% in contrast with the CNNs' 87.5%, but with a processing time of 80 ms as to 120 ms, energy consumption of 10 mJ in comparison to the CNNs' 15mJ. Also, quantum-inspired random forests lowered processing delay by 40%, sustaining superior accuracy than the classical-base system. These results demonstrate that QiML can effectively balance computation complexity while making it suitable for edge computing, IoT, and mobile intelligence systems. 2025 IEEE.
Source
Proceedings of 8th International Conference on Trends in Electronics and Informatics, ICOEI 2025;pp.1715-1724
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
AI Optimization; Edge Computing; Machine Learning; Mobile Intelligence; Quantum Algorithms; Quantum Computing; Real-Time Data; Signal Processing
Coverage
Kolluru V., Stevens Institute of Technology, Department of Data Science, NJ, United States; Helen Parimala E., Gitam Deemed to be University, Department of Computer Science, Bangalore, India; Kokilavani T., School of Sciences, Christ (Deemed) University, Department of Computer Science, Bangalore, India; Sunil Raj Y., St. Xavier's College (Autonomous), Department of Data Science, Palayamkottai, India; Jesuraj B., Mother Theresa Arts and Science College, Department of Computer Science, Tamilnadu, Theni, India; Aminuddin A., Universitas Amikom Yogyakarta, Department of Information System, Faculty of Computer Science, Sleman, 55283, Indonesia
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833154460-7;
Format
online
Language
English
Type
Conference paper

Citation

Kolluru, Vinothkumar; Helen Parimala, E.; Kokilavani, T.; Sunil Raj, Y.; Jesuraj, Bastin; Aminuddin, Afrig, “QiMINT: Quantum-Inspired Mobile Intelligence - Advancing Complex Signal Processing with Machine Learning,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26070.

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