Joint Quantum Reinforcement Learning and Neural Myerson Auction for High-Quality Digital-Twin Services in Multitier Networks

In order to build realistic digital-twin systems, this article proposes a novel two-stage algorithm for high-quality digital-twin services in cloud-assisted multitier networks. In our proposed algorithm, the first stage is quantum multiagent reinforcement learning (QMARL)-based scheduling for differentiated quality control of individual segments of digital-twin virtual objects in our cloud. As the number of segments selected by each edge increases, the edge’s action dimension expands exponentially, posing significant challenges to learning with conventional MARL. To solve this problem, the quantum-inspired MARL-based scheduler is considered in order to reduce the scheduling action dimensions into a logarithmic-scale. For the scheduling formulation, age-of-information (AoI) is also considered for low-latency high-quality digital-twin services. Additionally, the second stage is for the fast and seamless distribution of differentiated quality-controlled segments of virtual objects. For this objective, each user requests its desired segments and one of nearby edges is selected. Among various approaches, this second stage considers second price auction for truthful and distributed computation. Furthermore, low-complexity computation can be realized by avoiding integer-programming-based computation which is NP-hard. The proposed two-stage algorithm achieves performance levels that are 8.33 and 1.18 times higher in terms of reward value in high dimensions and revenue, respectively, compared to other benchmarks.