Securing SIM-Assisted Wireless Networks via Quantum Reinforcement Learning

TL;DR

A hybrid quantum proximal policy optimization (Q-PPO) framework for SIM-assisted secure communications is proposed, which jointly optimizes transmit power allocation and SIM phase shifts to maximize the average secrecy rate under power and quality-of-service constraints.

Abstract

Stacked intelligent metasurfaces (SIMs) have recently emerged as a powerful wave-domain technology that enables multi-stage manipulation of electromagnetic signals through multilayer programmable architectures. While SIMs offer unprecedented degrees of freedom for enhancing physical-layer security, their extremely large number of meta-atoms leads to a high-dimensional and strongly coupled optimization space, making conventional design approaches inefficient and difficult to scale. Moreover, existing deep reinforcement learning (DRL) techniques suffer from slow convergence and performance degradation in dynamic wireless environments with imperfect knowledge of passive eavesdroppers. To overcome these challenges, we propose a hybrid quantum proximal policy optimization (Q-PPO) framework for SIM-assisted secure communications, which jointly optimizes transmit power allocation and SIM phase shifts to maximize the average secrecy rate under power and quality-of-service constraints. Specifically, a parameterized quantum circuit is embedded into the actor network, forming a hybrid classical-quantum policy architecture that enhances policy representation capability and exploration efficiency in high-dimensional continuous action spaces. Extensive simulations demonstrate that the proposed Q-PPO scheme consistently outperforms DRL baselines, achieving approximately 15% higher secrecy rates and 30% faster convergence under imperfect eavesdropper channel state information. These results establish Q-PPO as a powerful optimization paradigm for SIM-enabled secure wireless networks.

Figures (6)

• Figure 1: The SIM-aided secure multi-user communication system model.
• Figure 2: The proposed hybrid quantum-classical Q-PPO framework.
• Figure 3: The convergence of the different algorithms.
• Figure 4: Comparison of proposed Q-PPO and benchmark schemes (PPO and Random), where we consider varying (a) learning rate $lr$, (b) number of available qubits $q$ and (c) number of PQC layers $\eta$.
• Figure 5: Evaluation of the average secrecy rate with varying (a) number of meta atoms per layers ($N$), (b) number of SIM layers ($L$), (c) maximum transmit power ($P_0$), (d) minimum data rate requirement ($R_{\min}$), (e) number of CUs ($M$), and (f) CUs/Eva distance ($d_{\epsilon}$).