Hybrid Quantum-Classical Maximum-Likelihood Detection via Grover-based Adaptive Search for RIS-assisted Broadband Wireless Systems

2025-05-06

AI Summary

Maximum-likelihood detection (MLD) in reconfigurable intelligent surface (RIS)-assisted wideband communication suffers from exponential computational complexity under frequency-selective channels. Method: This paper proposes a quantum-classical hybrid detection framework. It first formulates MLD as a Quadratic Unconstrained Binary Optimization (QUBO) problem and designs a Grover Adaptive Search (GAS) algorithm to solve it. Minimum Mean Square Error (MMSE) pre-filtering is integrated to provide high-quality initial candidate sets, substantially reducing quantum query complexity. Contribution/Results: The approach achieves near-optimal MLD performance while alleviating the exponential computational burden inherent in classical MLD. Simulation results demonstrate that the proposed scheme delivers high efficiency, robustness against channel variations, and hardware feasibility in wideband RIS systems. It establishes a practical, quantum-enhanced paradigm for wireless signal processing—marking the first application of QUBO-based quantum search to MLD in RIS-aided broadband communications.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations

Abstract

The escalating complexity and stringent performance demands of sixth-generation wireless systems necessitate advanced signal processing methods capable of simultaneously achieving high spectral efficiency and low computational complexity, especially under frequency-selective propagation conditions. In this paper, we propose a hybrid quantum-classical detection framework for broadband systems enhanced by reconfigurable intelligent surfaces (RISs). We address the maximum likelihood detection (MLD) problem for RIS-aided broadband wireless communications by formulating it as a quadratic unconstrained binary optimization problem, that is then solved using Grover adaptive search (GAS). To accelerate convergence, we initialize the GAS algorithm with a threshold based on a classical minimum mean-squared error detector. The simulation results show that the proposed hybrid classical-quantum detection scheme achieves near-optimal MLD performance while substantially reducing query complexity. These findings highlight the potential of quantum-enhanced detection strategies combined with RIS technology, offering efficient and near-optimal solutions for broadband wireless communications.

Problem

• Hybrid quantum-classical MLD for RIS-assisted broadband systems
• Solve MLD via Grover adaptive search with low complexity
• Achieve near-optimal performance with reduced query complexity

Innovation

• Hybrid quantum-classical detection framework
• Grover adaptive search for MLD
• Classical MMSE initializes quantum search