🤖 AI Summary
This study addresses the industrial applicability of quantum computing to combinatorial optimization problems. It provides a systematic review of mainstream quantum optimization approaches—including quantum annealing, the Quantum Approximate Optimization Algorithm (QAOA), Quantum Reinforcement Learning (QRL), and Quantum Generative Modeling (QGM)—and, for the first time, aligns these methods precisely with real-world domains such as logistics, finance, and telecommunications. Leveraging authoritative benchmarking platforms like QOBLIB and QUARK, the work evaluates the engineering maturity and empirical evidence of quantum advantage across these algorithms. Findings indicate that quantum annealing currently offers the highest practical utility, QAOA demonstrates feasibility on NISQ devices, and QRL and QGM represent promising high-impact directions for future development. This research establishes a comprehensive evaluation framework and strategic roadmap for the industrial deployment of quantum optimization technologies.
📝 Abstract
In this work, we review quantum approaches to combinatorial optimization, with the aim of bridging theoretical developments and industrial relevance. We first survey the main families of quantum algorithms, including Quantum Annealing, the Quantum Approximate Optimization Algorithm (QAOA), Quantum Reinforcement Learning (QRL), and Quantum Generative Modeling (QGM). We then examine the problem classes where quantum technologies currently show evidence of quantum advantage, drawing on established benchmarking initiatives such as QOBLIB, QUARK, QASMBench, and QED-C. These problem classes are subsequently mapped to representative industrial domains, including logistics, finance, and telecommunications. Our analysis indicates that quantum annealing currently exhibits the highest level of operational maturity, while QAOA shows promising potential on NISQ-era hardware. In contrast, QRL and QGM emerge as longer-term research directions with significant potential for future industrial impact.