Mapping Quantum Computing Techniques for <scp>NP</scp> ‐Hard Problems in Operations Management and Operations Research

ABSTRACT This study examines the fragmented and rapidly evolving body of knowledge on the application of quantum computing to NP‐hard decision problems in Operations Management (OM) and Operations Research (OR). It aims to systematically map how quantum optimization approaches are formulated and applied across core OM/OR problem classes, highlighting current advances and unresolved challenges for research and practice. A systematic mapping review was conducted using peer‐reviewed studies indexed in Scopus and Web of Science from 2014 to 2026. The literature was classified by problem type, mathematical formulation, quantum technique, and application domain, with attention to the alignment between quantum models and established OM/OR decision frameworks. The review reveals a strong predominance of QUBO‐based formulations and annealing‐oriented approaches, mainly applied to logistics, manufacturing, and financial optimization problems. Applications remain largely exploratory, with limited empirical validation, weak theoretical integration with OM/OR decision‐making models, and persistent challenges related to scalability and hybrid quantum‐classical performance. Only a small subset of studies demonstrates how quantum formulations can support real‐world scheduling and resource coordination problems. This study proposes an analytical framework linking quantum optimization paradigms to canonical OM/OR NP‐hard problem classes, identifying key research gaps and methodological tensions to support more theory‐driven and empirically grounded future applications, especially in complex decision environments.