Self-provisioning of network services with quantum-inspired reinforcement learning and adaptation

In this paper, a quantum-inspired reinforcement learning (QiRL) algorithm is proposed for self-configuring network services in next generation networks. A new learning and adaptation scheme based on QiRL facilitates the optimal operation for multiple classes of managed elements on a network Operations Support Systems (OSSs). QiRL algorithm adopts a probabilistic action selection policy and a new reinforcement strategy inspired by amplitude amplification in quantum computation. It is also characterized by learning and adaptation capabilities against dynamic environment changes and uncertainties. The algorithm is adapted to be suitable for the network service configuration process, which is simply redefined as: the managed elements represented as graphic nodes, and aware of the environment, select nodes with the minimum cost constraints until the eligible network elements are located along near-optimal paths; the located elements are those needed for the configuration or activation of a particular product and service. The results demonstrate the effectiveness of the proposed method.