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

TL;DR

This paper introduces a quantum meta-learning algorithm for RIS optimization that adaptively selects quantum paths to improve wireless communication performance in dynamic environments.

Contribution

It presents a hierarchical quantum meta-learning approach that learns to select and recombine optimal RIS configurations using quantum states and superposition.

Findings

Enhanced spectral efficiency demonstrated in simulations

Faster convergence compared to classical methods

Improved adaptability in dynamic wireless scenarios

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.