Grover Adaptive Search for Maximum Likelihood Detection of Generalized Spatial Modulation

TL;DR

This paper introduces a quantum-assisted method using Grover adaptive search to improve maximum likelihood detection of generalized spatial modulation signals, potentially outperforming classical methods for large data sets.

Contribution

The paper formulates GSM detection as a polynomial optimization problem and applies a quantum algorithm, demonstrating potential advantages over classical solutions.

Findings

Quantum algorithm reduces query complexity for large data sets

Proposed method outperforms classical solutions in numerical analysis

Performance gains are significant with increased data symbols and constellation size

Abstract

We propose a quantum-assisted solution for the maximum likelihood detection (MLD) of generalized spatial modulation (GSM) signals. Specifically, the MLD of GSM is first formulated as a novel polynomial optimization problem, followed by the application of a quantum algorithm, namely, the Grover adaptive search. The performance in terms of query complexity of the proposed method is evaluated and compared to the classical alternative via a numerical analysis, which reveals that under fault-tolerant quantum computation, the proposed method outperforms the classical solution if the number of data symbols and the constellation size are relatively large.