This chapter discusses a systematic review of the literature available on quantum computers from their theoretical bases through their current advances to their trends in the future. It uses the PRISMA approach to collect such writings from many fields, including physics, computer science, and engineering. It identifies crucial breakthroughs, including Feynman’s conjecture and Shor’s algorithm, and examines how they have affected modern quantum computing paradigms. The said focus areas will center on quantum algorithms, hardware developments in quantum information technology, cryptography, and machine learning, all with an emphasis on their combined contribution to the growth of the field. Identified milestones include achievements of the so-called quantum supremacy and advances made possible by qubit technologies, such as superconducting circuits and ion traps. The review not only presents future challenges, such as scalability, error correction, and noise management, but also pinpoints research gaps and possible avenues for subsequent exploration. The chapter examines the themes of interdisciplinary collaboration and funding trends as well as emerging issues such as quantum cloud computing and hybrid quantum-classical models. Such an overview of the trajectory of quantum computing by this review is also an academic reference as well as a strategic guide for researchers, policymakers, and technologists traversing this fast-evolving field.
This chapter discusses a systematic review of the literature available on quantum computers from their theoretical bases through their current advances to their trends in the future. It uses the PRISMA approach to collect such writings from many fields, including physics, computer science, and engineering. It identifies crucial breakthroughs, including Feynman’s conjecture and Shor’s algorithm, and examines how they have affected modern quantum computing paradigms. The said focus areas will center on quantum algorithms, hardware developments in quantum information technology, cryptography, and machine learning, all with an emphasis on their combined contribution to the growth of the field. Identified milestones include achievements of the so-called quantum supremacy and advances made possible by qubit technologies, such as superconducting circuits and ion traps. The review not only presents future challenges, such as scalability, error correction, and noise management, but also pinpoints research gaps and possible avenues for subsequent exploration. The chapter examines the themes of interdisciplinary collaboration and funding trends as well as emerging issues such as quantum cloud computing and hybrid quantum-classical models. Such an overview of the trajectory of quantum computing by this review is also an academic reference as well as a strategic guide for researchers, policymakers, and technologists traversing this fast-evolving field.
Quantum computing represents one of the most transformative technological shifts in computational science since the advent of classical computing architectures. This systematic literature review (SLR) synthesizes 212 peer-reviewed studies published between 2015 and 2024 to map the current landscape of quantum computing research as it intersects with software development practices, tools, frameworks, and methodologies. Following the PRISMA 2020 guidelines, we searched six major academic databases and identified relevant literature through a rigorous multi-stage screening process. Our analysis reveals five dominant research themes: quantum algorithm development and software frameworks, quantum machine learning integration, quantum cryptography and security applications, quantum software testing and verification, and hybrid quantum-classical software architectures. We further document major challenges including hardware decoherence, limited qubit availability, the absence of mature quantum DevOps pipelines, and a critical shortage of quantum-competent software engineers. The review concludes with a structured research agenda identifying six high-priority gaps where future scholarly work is most urgently needed. This paper provides a foundational reference for software engineers, researchers, and technology strategists seeking to understand and navigate the rapidly evolving quantum software ecosystem.
Quantum computing is at the cutting edge of technological innovation, with the potential to transform a wide array of scientific and practical fields. This literature survey addresses the critical need for a structured understanding of recent advancements and identifies research gaps. It comprehensively reviews recent advancements in quantum computing, emphasizing non-standard architectures, applications in material sciences, and the integration of quantum and classical machine learning models. The survey also examines quantum algorithms for real-time systems, explores cryptographic protocols beyond Shor's algorithm, and discusses resource management in cloud computing. Key findings highlight significant progress in algorithm efficiency, practical applications in 2D materials and topological insulators, and the hybridization of quantum and classical models. Challenges such as scalability, resource optimization, and ethical concerns still remain. This paper guides future research to focus on optimizing algorithms, efficient resource allocation, and enhancing security measures. This work synthesizes current knowledge, guiding future research efforts and contributing to the advancement of the quantum computing research community.
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 is a paradigm shift in computational science, where computational tasks that cannot be computationally tackled with binary computing systems can be tackled using quantum mechanics principles, including superposition, entanglement, and quantum interference. The present paper gives a comprehensive summary of the present condition of quantum computing, including the most advanced hardware systems, such as topological qubits, photonics processors, trapped-ion systems and superconducting qubits, and their corresponding advantages and technical challenges. Also, quantum algorithms, including the Quantum Approximate Optimization Algorithm (QAOA) and the variational quantum eigen solvers (VQE), include a summary of the first algorithm, Shor's factorization method, and the second algorithm, Grover's search algorithm. Applications of such algorithms are evaluated in such areas as financial modeling, machine learning, material science, drug discovery, and cryptography. Taking into account the information provided by large research facilities and technology firms, the conditions under which quantum systems can outperform conventional technology, such a concept as quantum advantage is given specific focus in this review. Moreover, this paper pays particular attention to the key barriers to effective implementation, such as qubit decoherence, large error rates, scalability issues, and the lack of fault-tolerant quantum devices that can be sold commercially. Regarding the noisy intermediate-scale quantum (NISQ) devices that are expected to be available in the near future, we discuss novel approaches to quantum error correction, especially surface code and concatenated code. The quantum software industry, such as IBM Qiskit, Google Cirq, and Microsoft Q, and an evaluation of quantum workforce and research facility readiness across the globe is also discussed. In the end, this work highlights essential potential future paths, such as standards for post-quantum cryptography, hybrid quantum-classical computation, quantum networking and the quantum internet, and the long-term aims for universal fault- tolerant quantum computers. The review should provide researchers, graduate students, and practitioners with a systematic and up-to-date base to understand the fast-growing discipline of quantum computing and its extensive implications on science and business.
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 computational model capable of addressing problems intractable for classical systems. This chapter explores the foundational principles, hardware implementations, and algorithmic approaches that define the field of quantum computing. Key quantum mechanical phenomena – such as superposition, entanglement, and interference – are examined in relation to their computational applications. The discussion extends to qubit realization technologies including superconducting circuits, trapped ions, photonic systems, and emerging platforms like topological qubits. Core quantum algorithms, including Shor’s and Grover’s, are analyzed to demonstrate their operational mechanics and performance advantages. The chapter also presents current challenges in quantum error correction, coherence preservation, and hardware scalability, particularly in the context of noisy intermediate-scale quantum devices. Further, it highlights applications across domains such as cryptography, optimization, quantum chemistry, and machine learning. The content is supported by illustrative diagrams, comparative tables, and up-to-date research references, providing a comprehensive overview of the state of quantum computing and its future directions.