Quantum computing exploits quantum-mechanical principles such as entanglement and superposition to offer significant computational advantages over conventional classical computing. Many complex and computationally challenging problems are expected to be solved by quantum computing in a number of fields, such as data science, industrial chemistry, smart energy, finance, secure communications, and many others. In order to understand the current status of quantum computing and identify its challenges, a systematic review of the existing literature will be valuable. An overview of quantum computing literature and its taxonomy is presented in this chapter. Further, the proposed taxonomy aims to identify research gaps by mapping various related studies. There is a detailed analysis of quantum technologies with the most current state of the art. Finally, the chapter presents a highlight of open challenges and future research directions.
Quantum computing exploits quantum-mechanical principles such as entanglement and superposition to offer significant computational advantages over conventional classical computing. Many complex and computationally challenging problems are expected to be solved by quantum computing in a number of fields, such as data science, industrial chemistry, smart energy, finance, secure communications, and many others. In order to understand the current status of quantum computing and identify its challenges, a systematic review of the existing literature will be valuable. An overview of quantum computing literature and its taxonomy is presented in this chapter. Further, the proposed taxonomy aims to identify research gaps by mapping various related studies. There is a detailed analysis of quantum technologies with the most current state of the art. Finally, the chapter presents a highlight of open challenges and future research directions.
Abstract Quantum computing (QC) is an emerging paradigm with the potential to offer significant computational advantage over conventional classical computing by exploiting quantum‐mechanical principles such as entanglement and superposition. It is anticipated that this computational advantage of QC will help to solve many complex and computationally intractable problems in several application domains such as drug design, data science, clean energy, finance, industrial chemical development, secure communications, and quantum chemistry. In recent years, tremendous progress in both quantum hardware development and quantum software/algorithm has brought QC much closer to reality. Indeed, the demonstration of quantum supremacy marks a significant milestone in the Noisy Intermediate Scale Quantum (NISQ) era—the next logical step being the quantum advantage whereby quantum computers solve a real‐world problem much more efficiently than classical computing. As the quantum devices are expected to steadily scale up in the next few years, quantum decoherence and qubit interconnectivity are two of the major challenges to achieve quantum advantage in the NISQ era. QC is a highly topical and fast‐moving field of research with significant ongoing progress in all facets. A systematic review of the existing literature on QC will be invaluable to understand the state‐of‐the‐art of this emerging field and identify open challenges for the QC community to address in the coming years. This article presents a comprehensive review of QC literature and proposes taxonomy of QC. The proposed taxonomy is used to map various related studies to identify the research gaps. A detailed overview of quantum software tools and technologies, post‐quantum cryptography, and quantum computer hardware development captures the current state‐of‐the‐art in the respective areas. The article identifies and highlights various open challenges and promising future directions for research and innovation in QC.
Quantum computing is an emerging paradigm that leverages the principles of quantum mechanics to solve computational problems beyond the reach of classical computers. This article provides an overview of the fundamental concepts of qubits, the distinctive features of quantum mechanics such as superposition and entanglement, and the challenges of building scalable, fault-tolerant systems. It surveys key quantum algorithms and their potential applications in fields including cryptography, optimization, finance, chemistry, and machine learning. Additionally, it highlights the importance of verification frameworks for ensuring the reliability of quantum programs. A literature review of significant contributions is presented, drawing insights from recent surveys on quantum algorithms, qubit technologies, and software verification approaches. The article concludes by discussing ongoing challenges, such as error correction overhead, hardware scalability, and verification complexity, and suggests directions for future research.
Quantum computing is a groundbreaking technology that leverages the properties of quantum mechanics, such as superposition, interference, and entanglement, to perform computations that exceed the capabilities of classical computers. Researchers have been exploring various application areas of quantum computing, with significant advancements contributing to the field of data security. Quantum algorithms threaten traditional encryption systems by being able to break encryption methods currently considered secure. To address such challenges, this chapter explores the fundamental principles of quantum mechanics utilized in quantum computing and reviews the vulnerabilities of underlying mathematical problems that support current encryption techniques. Additionally, We have explained how quantum algorithms can break these systems and reviewed state-of-the-art strategies to develop stronger post-quantum cryptographic solutions. Finally, we have highlighted key applications of quantum computing in securing data and outlined future research directions to prepare for quantum-related challenges.
Quantum computing has emerged as a groundbreaking paradigm in computational science, offering unprecedented capabilities to tackle complex problems that are beyond the reach of classical computing. This paper provides a comprehensive overview of quantum computing technologies and their uses across various domains. We begin by elucidating the fundamental principles of quantum mechanics that underpin quantum computing, including superposition, entanglement, and quantum interference. Subsequently, we delve into the key components of quantum computing architectures, such as qubits, quantum gates, and quantum circuits, highlighting the advancements in hardware implementations, including superconducting qubits, trapped ions, and photonic systems. Finally, we survey a wide array uses of quantum computing across fields such as cryptography, optimization, machine learning, chemistry, and finance, showcasing the transformative potential of this revolutionary technology. Through this survey, we aim to provide researchers, practitioners, and enthusiasts with a comprehensive understanding of the state-of-the-art in quantum computing technologies and their burgeoning applications, thereby fostering further advancements and innovations in this rapidly evolving field.
The rapid advancement of quantum computing has, without question, outpaced the rate of the subsequent setting up of an appropriate framework to categorize its various technologies. This has resulted in essentially fragmented terminologies with research efforts being considered in isolation, limiting collaboration or adaptation by industry. This chapter intends to fill the gap by introducing a multidimensional taxonomic approach to help develop a coherent and standardized framework for terminology in quantum computing. The proposed framework separates itself from existing frameworks by unifying four significant dimensions: computational models, hardware implementations, algorithmic strategies, and application domains. Its modular nature promotes relative ease of adaptation to emerging technologies, such as diamond-based qubits and cloud-based quantum platforms. The theoretical framework upon which this model has been developed is based on systematic literature review, domain analysis, and expert consultation. Using comparative benchmarking and real-world scenarios, the framework documents its flexibility in coordinating technical capabilities with market demands. For academia, it offers a structured curriculum tool and research roadmap, whereas, for industry, it clarifies investment priorities and benchmarks technological advancements. The identified challenges such as complexity and maintenance demands, are acknowledged with solutions proposed through dynamic updating mechanisms and collaborative governance. Providing a common terminology and adaptable structure not only organizes present knowledge but also makes possible a faster transfer of theoretical breakthroughs to social impact. It allows stakeholders to conduct complex tasks on quantum computing technology and ensure equitable and sustainable utilization of its future transformative potential.
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.