A Quantum Technology for Reinforcement Learning on Channel Assignment

Abstract

Prospective application of quantum technologies for reinforcement learning (RL) is an exciting surge in quantum fields. Quantum random number generators (QRNGs) produce high-frequency random bits, with advantages of true randomness and device independence over pseudo-random numbers. This work explores a new approach, called the quantum random numbers for multi-armed bandit (QRN-MAB) algorithm, for multi-user access in wireless communication system upon random bits. The primary objective of the algorithm is to attain a stable assignment state without further exchange. QRN-MAB utilizes random bits to learn channel features and conducts concurrent exchange to attain stability. This work finds that the intrinsic randomness property used in QRN-MAB enables arrangement to maintain high accuracy and outperforms other classical algorithms. Additionally, the algorithm exhibits strong adaptability when the environment changes over time and quantum random numbers are advantageous over other pseudo-random methods in achieving the target. This work provides an effective way for quantum technologies applications in RL and unfolds a promising avenue to stabilize assignment among multiple users.