# Securing SIM-Assisted Wireless Networks via Quantum Reinforcement Learning

arXiv: https://arxiv.org/abs/2602.13238 · HTML: https://mlsanity.com/paper/2602.13238/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 63 references that are in the map.
- First posted on arXiv 29 Jan 2026.

## Builds on (most cited first, 26 of 26)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [All-Optical Machine Learning Using Diffractive Deep Neural Networks](https://mlsanity.com/paper/1804.08711/) (2018)
- [Parametrized quantum policies for reinforcement learning](https://mlsanity.com/paper/2103.05577/) (2021)
- [Introduction to Quantum Reinforcement Learning: Theory and PennyLane-based Implementation](https://mlsanity.com/paper/2108.06849/) (2021)
- [Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks](https://mlsanity.com/paper/1912.11999/) (2019)
- [Stacked Intelligent Metasurfaces for Efficient Holographic MIMO Communications in 6G](https://mlsanity.com/paper/2305.08079/) (2023)
- [Multi-hop RIS-Empowered Terahertz Communications: A DRL-based Hybrid Beamforming Design](https://mlsanity.com/paper/2101.09137/) (2021)
- [An Introduction to Quantum Reinforcement Learning (QRL)](https://mlsanity.com/paper/2409.05846/) (2024)
- [Distortion Resilience for Goal-Oriented Semantic Communication](https://mlsanity.com/paper/2309.14587/) (2023)
- [Multi-User MISO with Stacked Intelligent Metasurfaces: A DRL-Based Sum-Rate Optimization Approach](https://mlsanity.com/paper/2408.04837/) (2024)
- [Stacked Intelligent Metasurfaces for Wireless Communications: Applications and Challenges](https://mlsanity.com/paper/2407.03566/) (2024)
- [Emerging Technologies in Intelligent Metasurfaces: Shaping the Future of Wireless Communications](https://mlsanity.com/paper/2411.19754/) (2024)
- [Robust Transmit Beamforming for Secure Integrated Sensing and Communication](https://mlsanity.com/paper/2207.13863/) (2022)
- [Stacked Intelligent Metasurfaces for Multiuser Beamforming in the Wave Domain](https://mlsanity.com/paper/2302.03188/) (2023)
- [Stacked Intelligent Metasurfaces for Multiuser Downlink Beamforming in the Wave Domain](https://mlsanity.com/paper/2309.02687/) (2023)
- [Q-Policy: Quantum-Enhanced Policy Evaluation for Scalable Reinforcement Learning](https://mlsanity.com/paper/2505.11862/) (2025)
- [Robust Transmission Design for Intelligent Reflecting Surface Aided Secure Communication Systems with Imperfect Cascaded CSI](https://mlsanity.com/paper/2004.11580/) (2020)
- [Terahertz Communications for Massive Connectivity and Security in 6G and Beyond Era](https://mlsanity.com/paper/2210.13872/) (2022)
- [Fairness Enhancement of UAV Systems with Hybrid Active-Passive RIS](https://mlsanity.com/paper/2306.14008/) (2023)
- [Hybrid Digital-Wave Domain Channel Estimator for Stacked Intelligent Metasurface Enabled Multi-User MISO Systems](https://mlsanity.com/paper/2309.16204/) (2023)
- [Channel Estimation for Stacked Intelligent Metasurface-Assisted Wireless Networks](https://mlsanity.com/paper/2403.05870/) (2024)
- [Joint SIM Configuration and Power Allocation for Stacked Intelligent Metasurface-assisted MU-MISO Systems with TD3](https://mlsanity.com/paper/2408.05756/) (2024)
- [Dynamic Spectrum Access for Ambient Backscatter Communication-assisted D2D Systems with Quantum Reinforcement Learning](https://mlsanity.com/paper/2410.17971/) (2024)

## Abstract (arXiv)

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 address these challenges, we propose a hybrid quantum proximal policy optimization (QPPO) framework for SIM-assisted secure communications that 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.