Quantum computing is rapidly emerging as a new computing paradigm with the potential to improve decision-making, optimization, and simulation across industries. For industrial engineering (IE) and operations research (OR), this shift introduces both unprecedented opportunities and substantial challenges. The learning curve is high, and to help researchers navigate the emerging field of quantum operations research, we provide a road map of the current field of quantum operations research. We introduce the foundational principles of quantum computing, outline the current hardware and software landscape, and survey major algorithmic advances relevant to IE/OR, including quantum approaches to linear algebra, optimization, machine learning, and stochastic simulation. We then highlight applied research directions, including the importance of problem domains for driving long-term value of quantum computers and how existing classical OR models can be reformulated for quantum hardware. Recognizing the steep learning curve, we propose pathways for IE/OR researchers to develop technical fluency and engage in this interdisciplinary domain. By bridging theory with application, and emphasizing the interplay between hardware and research development, we argue that industrial engineers are uniquely positioned to shape the trajectory of quantum computing for practical problem-solving. Ultimately, we aim to lower the barrier to entry into quantum computing, motivate new collaborations, and chart future directions where quantum technologies may deliver tangible impact for industry and academia.
Quantum computing is rapidly emerging as a new computing paradigm with the potential to improve decision-making, optimization, and simulation across industries. For industrial engineering (IE) and operations research (OR), this shift introduces both unprecedented opportunities and substantial challenges. The learning curve is high, and to help researchers navigate the emerging field of quantum operations research, we provide a road map of the current field of quantum operations research. We introduce the foundational principles of quantum computing, outline the current hardware and software landscape, and survey major algorithmic advances relevant to IE/OR, including quantum approaches to linear algebra, optimization, machine learning, and stochastic simulation. We then highlight applied research directions, including the importance of problem domains for driving long-term value of quantum computers and how existing classical OR models can be reformulated for quantum hardware. Recognizing the steep learning curve, we propose pathways for IE/OR researchers to develop technical fluency and engage in this interdisciplinary domain. By bridging theory with application, and emphasizing the interplay between hardware and research development, we argue that industrial engineers are uniquely positioned to shape the trajectory of quantum computing for practical problem-solving. Ultimately, we aim to lower the barrier to entry into quantum computing, motivate new collaborations, and chart future directions where quantum technologies may deliver tangible impact for industry and academia.
Quantum computing has emerged as a transformative computational paradigm capable of addressing limitations increasingly encountered by classical computing systems as Moore’s law approaches physical and economic constraints. By leveraging quantum mechanical principles such as superposition, entanglement, and interference, quantum systems offer the potential for significant computational advantages in solving complex problems that are currently infeasible for conventional architectures. This review critically synthesizes recent advances in quantum computing by examining foundational principles, major algorithmic breakthroughs, evolving hardware platforms, and interdisciplinary real-world applications. A structured narrative literature review was conducted using major scientific databases, including PubMed, IEEE Xplore, Nature, ScienceDirect, and arXiv, with emphasis on peer-reviewed studies, landmark foundational research, and major industry developments published between 2018 and 2026. Key findings indicate substantial progress in superconducting, trapped ion, photonic, and topological quantum systems, alongside innovations in algorithms such as Shor’s, Grover’s, variational quantum methods, and quantum machine learning. Despite these advancements, major challenges including decoherence, noise, scalability limitations, and fault-tolerant error correction remain substantial barriers to commercial maturity. Future implications suggest quantum computing may profoundly influence artificial intelligence, cybersecurity, optimization, molecular simulation, and scientific discovery, particularly through hybrid classical-quantum systems and cloud-based democratization. Although practical deployment remains transitional, quantum computing is positioned to become a foundational technology with broad scientific, industrial, and societal implications.
Quantum computing has evolved from a largely theoretical discipline into a field of growing interest for commercial and industrial applications. Recent advances in quantum hardware, algorithms, and cloud-based access have enabled early experimentation outside academic environments, raising questions about its potential impact on real-world decision-making and operational problems. This paper provides an overview of the current state of quantum computing with a focus on its emerging relevance in commercial contexts. It examines the categories of problems that have attracted attention from industry, particularly those involving high combinatorial complexity such as optimization, scheduling, logistics, and resource allocation. The analysis also reviews the global landscape of companies and initiatives engaged in quantum computing, highlighting different technological approaches, maturity levels, and regional characteristics. In parallel, the paper discusses the principal challenges and limitations that constrain widespread adoption, including hardware immaturity, algorithmic readiness, cost factors, and organizational requirements. By considering both opportunities and risks, this study aims to present a balanced and realistic perspective on the role quantum computing may play in business environments. The objective is to clarify the current position of quantum computing within the broader technological landscape and to support informed interpretation of its potential, limitations, and future trajectory in commercial applications.
Quantum computing isn't merely another step forward in technology; it represents a leap into an entirely new paradigm for understanding information. By harnessing the strange but powerful principles of quantum mechanics, quantum computers use qubits that can exist in multiple states at once, connect instantly through entanglement, and solve certain problems far faster than any classical machine. This paper explores the fundamentals of quantum computing, from its theoretical roots to the breakthroughs and setbacks shaping its present. We examine the engineering and scientific challenges - from fragile qubits to daunting scalability issues - and the immense promise they hold for fields like cryptography, artificial intelligence, drug discovery, and materials science. Finally, we consider the future impact of this technology, both its opportunities and its risks, and how it may redefine industries, research methods, and even our approach to solving humanity's hardest problems. The purpose of this paper is to examine the fundamental principles, developmental trajectory, current challenges, and prospective impacts of quantum computing across scientific, industrial, and societal domains. Given its potential to reshape computational capabilities and address complex global challenges, this study holds significance for researchers, policymakers, and industry leaders seeking to understand and navigate the transition toward quantum technologies.
Quantum computing represents a paradigm shift in computational capabilities, promising to solve problems that are intractable for classical computers. This paper explores the architecture, implementation, and operational aspects of quantum computing, focusing on its applications in various fields. I conducted a comprehensive literature review to identify existing research gaps and propose methodologies for effective quantum computing implementation. My findings highlight the potential of quantum computing in optimization, cryptography, and machine learning, among other areas. This research paper provides a comprehensive examination of the architecture, implementation, and operational strategies of quantum computing, with a focus on its diverse applications in optimization, cryptography, and machine learning. Through an extensive literature review, I identify critical research gaps and propose innovative methodologies for effective quantum computing implementation. I conclude with strategic recommendations for future research directions, emphasizing the importance of enhancing quantum hardware, developing hybrid algorithms, and fostering interdisciplinary collaborations. This foundational study not only deepens the understanding of quantum computing’s current state but also paves the way for future advancements in this rapidly evolving field. The insights gained from the literature review, methodology, and results sections contribute to a deeper understanding of the current state of quantum computing and its potential applications.
Industrial engineering increasingly depends on solving large-scale optimization problems involving scheduling, routing, resource allocation, inventory control, energy management, and supply-chain coordination. Classical exact methods often become computationally expensive when problem size, uncertainty, and combinatorial complexity grow rapidly. Although practical quantum computers are still developing, quantum-inspired computing approaches have emerged as powerful alternatives that adapt principles from quantum mechanics to run on classical hardware. This paper investigates quantum-inspired computing approaches for complex optimization problems in industrial engineering. It examines methodologies including simulated annealing, tensor-network optimization, Ising formulations, quantum- inspired evolutionary algorithms, digital annealers, and hybrid classical-intelligent heuristics. Applications in production scheduling, facility layout, vehicle routing, maintenance planning, portfolio optimization, warehouse operations, and smart manufacturing are analyzed. Particular attention is given to solution quality, scalability, stochastic decision-making, real-time adaptability, and integration with Industry 4.0 data systems. Benefits include faster near-optimal solutions, improved operational efficiency, reduced energy consumption, and better responsiveness under uncertainty. Major challenges include formulation difficulty, parameter tuning, benchmarking inconsistency, interpretability, and transition barriers within traditional industrial workflows. A future roadmap is proposed involving hybrid quantum-classical ecosystems, AI- guided optimization engines, digital twins, and autonomous decision platforms. The paper concludes that quantum-inspired computing can deliver practical industrial value today by bridging the gap between conventional optimization and future quantum advantage, especially in environments where speed, flexibility, and complexity management are essential.
Quantum computing represents a transformative shift in computational paradigms by leveraging quantum mechanical phenomena such as superposition, entanglement, and quantum interference. Unlike classical computers, which rely on binary logic, quantum systems exploit these principles to solve certain problems more efficiently. This survey presents a comprehensive overview of the field, drawing from extensive literature spanning foundational quantum principles through key quantum algorithms, hardware platforms, and programming tools. The analysis bridges theoretical foundations with practical engineering challenges, examining both current capabilities and fundamental limitations of quantum systems. The paper also explores applications in cryptography, optimization, and quantum simulation, while addressing the ethical implications and long-term prospects of quantum technologies. Distinct from prior surveys, this work provides a unified perspective that connects quantum theory to practical implementation, supporting researchers and students in navigating the evolving landscape of quantum computing.