Quantum deep reinforcement learning for 6G mobile edge computing-based IoT systems

James Adu Ansere, Trung Q. Duong, Saeed R. Khosravirad, Vishal Sharma, Antonino Masaracchia, Octavia A. Dobre

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

This paper exploits a quantum-empowered machine learning algorithm to enhance computation learning speed. Under stochastic behaviours and quantum uncertainty, we examine the offloading problem to maximize the computational task processing efficiency, considering the computation latency, energy consumption, and quantum network adaptability. From the Markov decision process, the paper proposes a novel quantum-empowered deep reinforcement learning (Qe-DRL) approach, combining quantum computing theory and machine learning to achieve exploration and exploitation trade-off via quantum parallelism significantly. Furthermore, we develop a modified Grover’s algorithm with exponential convergence speed to provide a searching strategy for transition quantum states probabilities. Simulation results establish the effectiveness of the proposed QeDRL algorithm and its superior computational learning speed.

Original languageEnglish
Title of host publicationProceedings of the International Wireless Communications and Mobile Computing Conference, IWCMC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9798350333398
ISBN (Print)9798350333404
DOIs
Publication statusPublished - 21 Jul 2023
Event International Wireless Communications and Mobile Computing 2023 - Marrakesh, Morocco
Duration: 19 Jun 202323 Jun 2023

Publication series

NameInternational Wireless Communications and Mobile Computing Conference: Proceedings
ISSN (Print)2376-6492
ISSN (Electronic)2376-6506

Conference

Conference International Wireless Communications and Mobile Computing 2023
Abbreviated titleIWCMC 2023
Country/TerritoryMorocco
CityMarrakesh
Period19/06/202323/06/2023

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