Quantum computing is a deep technology whose progress cannot be driven effectively from one direction alone. While the field has developed a growing catalogue of mathematically grounded algorithmic speedups, industrial impact will depend just as much on starting from real industrial decision contexts and working downward to what must be computed, validated and integrated. In this Perspective, I argue that sustained progress requires treating these two directions: bottom-up development from physics, hardware and algorithms, and top-down development from industrial needs and constraints. Equally primary and continuously coupled. This dual-viewpoint is not a matter of balance for its own sake. Quantum computers cannot solve arbitrary problems, so engagement with industry must remain anchored in algorithmic tractability. Yet tractable computations are rarely valuable unless they connect to decision points in established workflows such as candidate selection in drug discovery or the design of a new aircraft shape with improved aerodynamics. I analyse how historical narratives and structural separations of expertise slowed the formation of this coupling and outline what it takes to build it: explicit interfaces between technical teams and domain context and intermediate layers that translate quantum outputs into decision-relevant observables without suffocating foundational innovation. Framed this way, quantum computing's opportunity is clearest where deep physical modelling meets high-value decisions. Provided the field co-designs both sides from the outset.
Quantum computing is a deep technology whose progress cannot be driven effectively from one direction alone. While the field has developed a growing catalogue of mathematically grounded algorithmic speedups, industrial impact will depend just as much on starting from real industrial decision contexts and working downward to what must be computed, validated and integrated. In this Perspective, I argue that sustained progress requires treating these two directions: bottom-up development from physics, hardware and algorithms, and top-down development from industrial needs and constraints. Equally primary and continuously coupled. This dual-viewpoint is not a matter of balance for its own sake. Quantum computers cannot solve arbitrary problems, so engagement with industry must remain anchored in algorithmic tractability. Yet tractable computations are rarely valuable unless they connect to decision points in established workflows such as candidate selection in drug discovery or the design of a new aircraft shape with improved aerodynamics. I analyse how historical narratives and structural separations of expertise slowed the formation of this coupling and outline what it takes to build it: explicit interfaces between technical teams and domain context and intermediate layers that translate quantum outputs into decision-relevant observables without suffocating foundational innovation. Framed this way, quantum computing's opportunity is clearest where deep physical modelling meets high-value decisions. Provided the field co-designs both sides from the outset.
Quantum computing has recently achieved experimental demonstrations of quantum advantage, marking a major milestone toward beyond-classical computation. However, translating these demonstrations into practical utility remains a central challenge. In this perspective, we argue that this transition is fundamentally limited by three interconnected gaps: the utility gap between benchmark tasks and real-world applications, the algorithmic gap between asymptotic quantum speedups and realistic performance, and the hardware gap imposed by noisy intermediate-scale devices. We further highlight that recent progress is beginning to narrow these gaps through the rapid maturation of quantum hardware, the emergence of resource-aware and practically grounded quantum algorithms, and the increasing integration of classical computation into the quantum workflow. Finally, we discuss potential early application domains for practical quantum advantage, while emphasizing that its ultimate realization will likely emerge from the co-evolution of hardware, algorithms, and application-driven discovery rather than any single breakthrough.
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 transitioning from laboratory research to industrial deployment, yet significant challenges persist: system scalability and performance, fabrication yields, and the advancement of algorithms and applications. We emphasize that in building quantum computers -- spanning quantum chips, system integration, instruction sets, algorithms, and middleware such as quantum error correction schemes -- design is everywhere. In this paper, we advocate for a holistic design perspective in quantum computing, a perspective we argue is pivotal to unlocking innovative co-design opportunities and addressing the aforementioned key challenges. To equip readers with sufficient background for exploring co-optimization opportunities, we detail how interconnected computational methods and tools collaborate to enable end-to-end quantum computer design. This coverage encompasses critical stages -- such as chip layout design automation, high-fidelity system-level simulation, Hamiltonian derivation for quantum system modeling, control pulse simulation, decoherence analysis, and physical verification and testing -- followed by quantum instruction set design. We then proceed to quantum system and software development, including quantum circuit synthesis, quantum error correction and fault tolerance, and logic verification and testing. Through these discussions, we illustrate with concrete examples -- including co-optimizing quantum instruction sets with algorithmic considerations, customizing error correction circuits to hardware-specific constraints, and streamlining quantum chip design through tailored code design, among others. We hope that the detailed end-to-end design workflow as well as these examples will foster dialogue between the hardware and software communities, ultimately facilitating the translation of meaningful research findings into future quantum hardware implementations.
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 The twenty-first century has witnessed remarkable advancements in computing technologies, transforming every aspect of modern society. As conventional computing systems approach their physical and computational limits, the search for new paradigms capable of solving increasingly complex problems has led to the emergence of Quantum Computing. By harnessing the principles of quantum mechanics, quantum computing represents a revolutionary approach that has the potential to redefine computation, communication, and information processing on an unprecedented scale. Quantum Computing is designed to provide readers with a comprehensive understanding of the concepts, principles, technologies, and applications that form the foundation of this rapidly evolving field. This book aims to bridge the gap between theoretical quantum mechanics and practical computational applications, enabling students, researchers, academicians, and professionals to explore the transformative potential of quantum technologies. The chapters in this book cover a broad spectrum of topics, including the fundamentals of quantum mechanics, quantum bits (qubits), quantum gates, superposition, entanglement, quantum circuits, quantum algorithms, quantum cryptography, quantum communication, and quantum error correction. The book also explores the architecture of quantum computers and examines the challenges involved in building scalable and fault-tolerant quantum systems. As industries increasingly seek innovative solutions to complex computational problems, quantum computing is emerging as a promising technology with applications in cryptography, artificial intelligence, machine learning, optimization, financial modeling, healthcare, pharmaceutical research, materials science, logistics, and climate modeling. The ability of quantum computers to process vast amounts of information simultaneously offers opportunities that were previously beyond the reach of classical computing systems. Special attention has been given to contemporary developments in quantum technologies, including quantum supremacy, hybrid quantum-classical computing, cloud-based quantum platforms, and the growing role of quantum computing in scientific discovery and industrial innovation. The book also discusses ethical, security, and societal implications associated with the advancement of quantum technologies. The primary objective of this book is to equip readers with both foundational knowledge and practical insights into the field of quantum computing. Through a balanced presentation of theory, applications, case studies, and emerging research trends, the book seeks to inspire curiosity, innovation, and further exploration in this transformative domain. We extend our sincere gratitude to the authors, reviewers, researchers, academicians, and industry experts whose valuable contributions have enriched the quality and relevance of this publication. Their dedication and expertise have helped create a resource that reflects the latest developments and future possibilities of quantum computing. We hope that Quantum Computing will serve as a valuable guide for students, educators, researchers, and technology professionals seeking to understand the next frontier of computational science. May this book inspire new ideas, foster innovation, and contribute to the advancement of knowledge in one of the most exciting technological revolutions of our time. Mr. M. Rajeshkumar Dr. R. Mariappan Mr. E.Janakiraman,
This book, Quantum Computing: Algorithms, Architectures and Applications, has been carefully developed as a comprehensive resource to navigate the dynamic and complex world of quantum technologies. Intended for students, researchers, engineers, and technology enthusiasts, it aims to connect fundamental concepts with real-world implementation. The journey begins with the core principles of quantum mechanics relevant to computing, laying the groundwork that differentiates quantum systems from classical approaches. It further explores quantum gates, circuits, and computational logic, along with key topics such as error correction, system architectures, entanglement, superposition, and the no-cloning theorem. As the discussion progresses, the book examines foundational quantum algorithms, including the Deutsch–Jozsa algorithm, Simon’s problem, and others. These sections not only explain technical concepts but also encourage readers to rethink computational possibilities and limitations. The book also highlights practical applications and industry adoption. Quantum computing is increasingly influencing fields such as cryptography, finance, drug discovery, and climate science. Through global and regional case studies, readers gain insight into how various sectors are embracing this technological shift. A clear and structured approach has been maintained throughout, making the content accessible to beginners while still valuable for experienced professionals. The aim is to balance depth with clarity, ensuring both rigor and ease of understanding. While quantum computing continues to evolve with ongoing advancements and discoveries, the driving forces behind its progress remain curiosity, innovation, and collaboration.