Quantum-Inspired Reinforcement Learning Models for Adaptive Decision-Making in Neuromorphic Computational Systems

Recent progress in quantum-inspired computing and neuromorphic machine has rekindled the quest to develop learning systems capable of probabilistic thinking, and which can be run with the energy efficiency of the brain. However, the vast majority of reinforcement learning (RL) models can be described as being constrained by strict update policies and high power usage when implemented on a spiking architecture or an edge-based architecture. To fill this gap, it is proposed in this study that a Quantum-Inspired Reinforcement Learning (QiRL) framework can combine the policy encoding based on amplitude, and the neuromorphic adaptation to reach real-time and low-energy decision-making. In contrast to the traditional RL, QiRL incorporates phase-modulated dynamics of the interference to enhance positive learning and discourage reward oscillation.