Public articles linked to the same research event.
arXiv Addressing the fact that base stations consume roughly 60-70% of radio access network energy in 5G and beyond networks, this article introduces a quantum reinforcement learning algorithm that uses superposition and entanglement through parameterized quantum circuits to optimize base-station energy use under dynamic user equipment behavior; simulations indicate it can substantially reduce energy consumption while maintaining quality of service and consistently outperforms deep reinforcement learning and Q-learning in convergence speed and learning complexity.
Addressing the fact that base stations consume roughly 60-70% of radio access network energy in 5G and beyond networks, this article introduces a quantum reinforcement learning algorithm that uses superposition and entanglement through parameterized quantum circuits to optimize base-station energy use under dynamic user equipment behavior; simulations indicate it can substantially reduce energy consumption while maintaining quality of service and consistently outperforms deep reinforcement learning and Q-learning in convergence speed and learning complexity.
Addressing the fact that base stations consume roughly 60-70% of radio access network energy in 5G and beyond networks, this article introduces a quantum reinforcement learning algorithm that uses superposition and entanglement through parameterized quantum circuits to optimize base-station energy use under dynamic user equipment behavior; simulations indicate it can substantially reduce energy consumption while maintaining quality of service and consistently outperforms deep reinforcement learning and Q-learning in convergence speed and learning complexity.
Addressing the fact that base stations consume roughly 60-70% of radio access network energy in 5G and beyond networks, this article introduces a quantum reinforcement learning algorithm that uses superposition and entanglement through parameterized quantum circuits to optimize base-station energy use under dynamic user equipment behavior; simulations indicate it can substantially reduce energy consumption while maintaining quality of service and consistently outperforms deep reinforcement learning and Q-learning in convergence speed and learning complexity.