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.
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.
As quantum computing (QC) continues to evolve in hardware and software, measuring progress in this complex and diverse field remains a challenge. To track progress, uncover bottlenecks, and evaluate community efforts, benchmarks play a crucial role. But which benchmarking approach best addresses the diverse perspectives of QC stakeholders? We conducted the most comprehensive systematic literature review of this area to date, combining NLP-based clustering with expert analysis to develop a novel taxonomy and definitions for QC benchmarking, aligned with the quantum stack and its stakeholders. In addition to organizing benchmarks in distinct hardware, software, and application focused categories, our taxonomy hierarchically classifies benchmarking protocols in clearly defined subcategories. We develop standard terminology and map the interdependencies of benchmark categories to create a holistic, unified picture of the quantum benchmarking landscape. Our analysis reveals recurring design patterns, exposes research gaps, and clarifies how benchmarking methods serve different stakeholders. By structuring the field and providing a common language, our work offers a foundation for coherent benchmark development, fairer evaluation, and stronger cross-disciplinary collaboration in QC.
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, built on the principles of quantum mechanics, has emerged as a transformative paradigm with the potential to revolutionize computational capabilities, enabling solutions to problems that are currently intractable for classical systems. Quantum computing integrates hardware, architectures, and software for improved applications while utilizing superposition, entanglement, and interference to promote materials science, cryptography, optimization, and artificial intelligence. Sections introduce and describe the model components needed in order to create quantum processors, including qubits, quantum gates and control electronics, as well as a review of new developments in quantum software frameworks and programming tools. Notable examples, such as Shor’s algorithm for cryptographic caveat, Grover’s algorithm for inefficient search processes, and quantum-enhanced optimization, illuminate the position of quantum computing as a means of tackling tough problems that span disciplines, such as pharmaceuticals, predictors of climate, and finance modeling. However, the field continues to face significant challenges, like limited scalability, quantum gate error rates, qubit coherence times, and difficulties integrating with classical systems. Future efforts will focus on developing hybrid quantum-classical systems, scalable architectures, and industry- specific applications to transition quantum computing from exploration to practical deployment. This review provides a comprehensive analysis of quantum computing, covering its potential, trends, and challenges, while exploring hardware-software integration, classicalquantum comparisons, and insights to guide research and industry in unlocking its revolutionary possibility.
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.
This paper presents a comprehensive systematic and bibliometric review of the rapidly expanding literature on quantum computing. The study addresses six research questions concerning prominent themes, temporal evolution, types of key findings, frequently reported research gaps, higher-order gap typologies, and future research directions. Utilizing a researcher-curated database, the final corpus comprises 153 studies compiled from non-Scopus, Scopus and Web of Science. The results demonstrate that quantum hardware and architecture, algorithms, and theoretical foundations dominate the current literature. Furthermore, publication output has risen significantly between 2018 and 2025. Among the reported findings, algorithmic innovations, performance claims, and hardware advancements are the most common. Despite this progress, the field faces substantial barriers; technical, scalability, fault-tolerance, and methodological-standardization gaps are the most frequently reported. Based on these identified gaps, eight actionable future research directions emerge to guide the scientific community. Ultimately, this review concludes that quantum computing is actively transitioning from a purely physics-driven domain into a multidisciplinary, engineering- and application-oriented science. However, its continued advancement is presently constrained by a hardware-methodology-application triad, wherein reporting practices often outpace empirical verification and standardized benchmarking.
Quantum computing has emerged as a paradigm-shifting innovation with the potential to transform computation across disciplines by exploiting principles such as superposition, entanglement, and quantum interference. Unlike classical computing, which processes binary data sequentially, quantum systems operate in massively parallel spaces, enabling solutions to problems once deemed computationally intractable.This study systematically explores the foundational mechanisms of quantum computing—particularly qubits, quantum gates, and error correction techniques—and evaluates its real-world applicability through a multidimensional framework. The research employs a combination of theoretical modeling, complexity analysis, and performance benchmarking to compare classical and quantum algorithms, with a focus on cryptographic resilience and artificial intelligence enhancement.Results demonstrate that quantum algorithms such as Shor’s and Grover’s offer exponential and quadratic speedups, respectively, for tasks like prime factorization and database search. The study further finds that Quantum Machine Learning (QML) models, especially Quantum Neural Networks, achieve superior classification accuracy and faster training times compared to classical models, as observed in pilot healthcare applications. Additionally, the research underscores significant vulnerabilities in existing cryptographic systems and advocates for urgent transitions toward post-quantum cryptography and Quantum Key Distribution.In conclusion, the findings affirm that quantum computing holds transformative potential across cybersecurity, artificial intelligence, and national infrastructure. However, challenges such as decoherence, hardware noise, and data encoding persist, necessitating sustained research and policy development. For emerging nations like Pakistan, strategic investment in quantum education, infrastructure, and international collaboration will be essential to harness the benefits and mitigate the risks of this revolutionary technology..