The quantum machine learning (QML) paradigms and their synergies with network
slicing can be envisioned to be a disruptive technology on the cusp of entering
to era of sixth-generation (6G), where the mobile communication systems are
underpinned in the form of advanced tenancy-based digital use-cases to meet
different service requirements. To overcome the challenges of massive slices
such as handling the increased dynamism, heterogeneity, amount of data,
extended training time, and variety of security levels for slice instances, the
power of quantum computing pursuing a distributed computation and learning can
be deemed as a promising prerequisite. In this intent, we propose a
cloud-native federated learning framework based on quantum deep reinforcement
learning (QDRL) where distributed decision agents deployed as micro-services at
the edge and cloud through Kubernetes infrastructure then are connected
dynamically to the radio access network (RAN). Specifically, the decision
agents leverage the remold of classical deep reinforcement learning (DRL)
algorithm into variational quantum circuits (VQCs) to obtain the optimal
cooperative control on slice resources. The initial numerical results show that
the proposed federated QDRL (FQDRL) scheme provides comparable performance than
benchmark solutions and reveals the quantum advantage in parameter reduction.
To the best of our knowledge, this is the first exploratory study considering
an FQDRL approach for 6G communication network.

本文提出了一种基于量子深度强化学习的云原生联邦学习框架，用于处理增加的 slice 处理量，并展示它与六代移动通信中性能相似的结果，从而揭示了参数减少的量子优势。