Path-Based Quantum Meta-Learning for Adaptive Optimization of Reconfigurable Intelligent Surfaces

2026-04-19

AI Summary

This work addresses the challenges of highly non-convex phase optimization, strong interference, and user mobility in reconfigurable intelligent surface (RIS)-assisted dynamic wireless environments by proposing a hierarchical multi-objective quantum meta-learning algorithm. Departing from conventional approaches that merely match historical configurations, the method models RIS control directions as inter-layer switching paths via a quantum neural network. It employs tensor product state encoding to compress high-dimensional environmental features into quantum states and leverages quantum superposition during path selection. A multi-objective scoring mechanism—integrating historical success rate, energy consumption, and current data rate—dynamically identifies the optimal path at each layer. Experimental results demonstrate that the proposed scheme significantly outperforms existing methods in spectral efficiency, convergence speed, and adaptability to dynamic environments.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Metareasoning and Metaheuristics
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Reconfigurable intelligent surfaces (RISs) modify signal reflections to enhance wireless communication capabilities. Classical RIS phase optimization is highly non convex and challenging in dynamic environments due to high interference and user mobility. Here we propose a hierarchical multi-objective quantum metalearning algorithm that switches among specific quantum paths based on historical success, energy cost, and current data rate. Candidate RIS control directions are arranged as switch paths between quantum neural network layers to minimize inference, and a scoring mechanism selects the top performing paths per layer. Instead of merely storing past successful settings of the RIS and picking the closest match when a new problem is encountered, the algorithm learns how to select and recombine the best parts of different solutions to solve new scenarios. In our model, high-dimensional RIS scenario features are compressed into a quantum state using the tensor product, then superimposed during quantum path selection, significantly improving quantum computational advantage. Results demonstrate efficient performance with enhanced spectral efficiency, convergence rate, and adaptability.

Problem

• Reconfigurable Intelligent Surfaces
• Phase Optimization
• Non-convex Optimization
• Dynamic Wireless Environments
• User Mobility

Innovation

• quantum meta-learning
• reconfigurable intelligent surfaces
• path-based optimization
• quantum neural networks
• adaptive wireless communication