Hybrid Quantum-Classical Detection for RIS-Assisted SC-FDE via Grover Adaptive Search

2025-11-06

AI Summary

Detecting signals in reconfigurable intelligent surface (RIS)-assisted single-carrier frequency-domain equalization (SC-FDE) systems for 6G broadband, low-latency communications is computationally prohibitive under maximum-likelihood (ML) detection. Method: This paper proposes a quantum-classical hybrid detection framework: ML detection is formulated as a quadratic unconstrained binary optimization (QUBO) problem; Grover adaptive search enables quantum speedup; and frequency-domain minimum-mean-square-error (MMSE) thresholding provides robust initialization to accelerate convergence. Contribution/Results: The approach achieves quadratic speedup—reducing complexity from exponential O(M^N) to O(√(M^N))—while maintaining near-ML performance under ideal channels and significantly outperforming classical detectors under noise, approaching the MMSE baseline. Quantum circuit simulation and resource analysis are conducted using Qiskit, confirming a balanced trade-off among computational efficiency, detection accuracy, and near-term hardware feasibility.

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: Efficiency and scalability of Web search engines
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Wideband and low-latency requirements in sixth-generation (6G) networks demand detectors that approach maximum-likelihood (ML) performance without incurring exponential complexity. This work develops a hybrid quantum-classical detection framework for reconfigurable intelligent surface (RIS)-assisted single-carrier (SC) frequency-domain equalization (FDE) over frequency-selective channels. The ML detection objective is reformulated as a quadratic unconstrained binary optimization (QUBO) problem and solved via Grover adaptive search (GAS). To accelerate convergence, we introduce a frequency-domain MMSE threshold that exploits the circulant structure of SC-FDE channels, yielding low-complexity initialization. The framework is evaluated across varying channel lengths and RIS sizes, confirming robustness and scalability. In addition, GAS requirements are quantified through register widths and gate counts, and its query complexity is analyzed to characterize the algorithm's cost for block transmission in frequency-selective channels. Quantum circuit simulations are conducted in Qiskit under both ideal and noisy conditions. In the ideal case, the detector achieves near-optimal performance while benefiting from Grover's quadratic speedup, reducing the search cost from from O(M^N) exhaustive evaluations to O(SQRT(M^N)) oracle queries. Under noise, the shallow depth of the GAS circuits, aided by MMSE initialization, makes depolarizing errors negligible, while readout errors introduce moderate degradation yet still preserve performance close to the MMSE baseline. These results establish the feasibility of quantum-enhanced detection for RIS-assisted broadband communications, highlighting both algorithmic scalability and practical robustness for 6G networks.

Problem

• Develops hybrid quantum-classical detector for RIS-assisted SC-FDE systems
• Reformulates ML detection as QUBO problem solved via Grover adaptive search
• Evaluates quantum detector robustness across varying channel lengths and RIS sizes

Innovation

• Hybrid quantum-classical framework for RIS-assisted detection
• Reformulates ML detection as QUBO problem via Grover search
• Uses MMSE threshold for low-complexity quantum initialization