Securing SIM-Assisted Wireless Networks via Quantum Reinforcement Learning

TL;DR

This paper introduces a hybrid quantum reinforcement learning framework to optimize the security of SIM-assisted wireless networks, significantly improving secrecy rates and convergence speed over traditional methods.

Contribution

It proposes a novel hybrid quantum-classical reinforcement learning approach for optimizing SIM-assisted secure communications, addressing high-dimensional challenges and dynamic environments.

Findings

Achieves approximately 15% higher secrecy rates than DRL baselines.

Provides 30% faster convergence in simulations.

Demonstrates robustness under imperfect eavesdropper channel information.

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.