TY - GEN
T1 - Quantum deep reinforcement learning for 6G mobile edge computing-based IoT systems
AU - Ansere, James Adu
AU - Duong, Trung Q.
AU - Khosravirad, Saeed R.
AU - Sharma, Vishal
AU - Masaracchia, Antonino
AU - Dobre, Octavia A.
PY - 2023/7/21
Y1 - 2023/7/21
N2 - 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.
AB - 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.
U2 - 10.1109/IWCMC58020.2023.10183020
DO - 10.1109/IWCMC58020.2023.10183020
M3 - Conference contribution
SN - 9798350333404
T3 - International Wireless Communications and Mobile Computing Conference: Proceedings
BT - Proceedings of the International Wireless Communications and Mobile Computing Conference, IWCMC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - International Wireless Communications and Mobile Computing 2023
Y2 - 19 June 2023 through 23 June 2023
ER -