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

TL;DR

This paper introduces a hybrid quantum-classical detection method for RIS-assisted broadband wireless systems, leveraging Grover-based adaptive search to achieve near-optimal performance with reduced complexity.

Contribution

It formulates the maximum likelihood detection as a quadratic unconstrained binary optimization problem and solves it using a Grover adaptive search with classical initialization.

Findings

Achieves near-optimal detection performance

Reduces query complexity significantly

Demonstrates potential of quantum-enhanced detection in wireless systems

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.