Abstract Quantum computing leverages fundamental quantum-mechanical phenomena—most notably superposition, entanglement, and quantum tunneling—to perform computations in ways that differ fundamentally from classical digital processing. Compared to conventional computing architectures, quantum systems promise significant reductions in computational time and potentially lower energy consumption for specific classes of problems. These advantages, combined with advances in nanoscale engineering and the slowing of traditional hardware scaling trends such as Moore’s Law, have led to substantial commercial and national-security-driven investments in quantum technologies over the past decade. A major milestone was reached when Google announced the experimental demonstration of “quantum supremacy,” showing that a quantum processor could execute a specific computational task infeasible for a classical supercomputer within practical time constraints. Although the demonstrated problem had no immediate practical application, this achievement marked a crucial step toward the development of quantum systems capable of delivering real-world computational advantages and provided a more precise technical framing of the concept of quantum advantage. Despite the fact that the mathematical foundations, programming models, and principal quantum algorithms were established in the 1990s, quantum computing remained largely confined to a specialized research community for decades. Today, the increasing accessibility of cloud-based quantum hardware calls for a broader dissemination of implementation-oriented knowledge among researchers and practitioners in computer science, software engineering, and related fields. The quantum programming paradigm differs fundamentally from classical programming models and often relies on physical and algebraic notations that may pose barriers to traditionally trained computer scientists. This series of articles provides a self-contained and concise introduction to quantum computing and its core algorithms, with a strong emphasis on practical implementation. Given that real quantum processors—such as those accessible via the IBM quantum ecosystem—are now available as cloud services, experimental validation can be conducted both on simulators and on actual hardware for small-scale inputs. While other platforms such as Rigetti and IonQ also offer cloud-based quantum computing services, this review focuses exclusively on the IBM quantum computing ecosystem.
Abstract Quantum computing leverages fundamental quantum-mechanical phenomena—most notably superposition, entanglement, and quantum tunneling—to perform computations in ways that differ fundamentally from classical digital processing. Compared to conventional computing architectures, quantum systems promise significant reductions in computational time and potentially lower energy consumption for specific classes of problems. These advantages, combined with advances in nanoscale engineering and the slowing of traditional hardware scaling trends such as Moore’s Law, have led to substantial commercial and national-security-driven investments in quantum technologies over the past decade. A major milestone was reached when Google announced the experimental demonstration of “quantum supremacy,” showing that a quantum processor could execute a specific computational task infeasible for a classical supercomputer within practical time constraints. Although the demonstrated problem had no immediate practical application, this achievement marked a crucial step toward the development of quantum systems capable of delivering real-world computational advantages and provided a more precise technical framing of the concept of quantum advantage. Despite the fact that the mathematical foundations, programming models, and principal quantum algorithms were established in the 1990s, quantum computing remained largely confined to a specialized research community for decades. Today, the increasing accessibility of cloud-based quantum hardware calls for a broader dissemination of implementation-oriented knowledge among researchers and practitioners in computer science, software engineering, and related fields. The quantum programming paradigm differs fundamentally from classical programming models and often relies on physical and algebraic notations that may pose barriers to traditionally trained computer scientists. This series of articles provides a self-contained and concise introduction to quantum computing and its core algorithms, with a strong emphasis on practical implementation. Given that real quantum processors—such as those accessible via the IBM quantum ecosystem—are now available as cloud services, experimental validation can be conducted both on simulators and on actual hardware for small-scale inputs. While other platforms such as Rigetti and IonQ also offer cloud-based quantum computing services, this review focuses exclusively on the IBM quantum computing ecosystem.
Quantum computing is poised to revolutionize the world of computation by har nessing the principles of quantum mechanics to solve problems that are beyond the capabilities of classical computers. Unlike traditional binary computing, which relies on bits that exist as 0s or 1s, quantum computing operates using qubits, which leverage superposition, entanglement, and quantum parallelism to perform computations exponentially faster. This transformative technology has applications in cryptography, artificial intelligence, materials science, and complex system simulations, offering unprecedented computational power. Despite its potential, quantum computing faces significant challenges, including hard ware stability, quantum decoherence, and error correction. Current quantum processors, such as those developed by IBM, Google, and D-Wave, have demonstrated quantum supremacy in specific tasks, but scaling quantum computers to practical levels remains a major hurdle. Ad ditionally, algorithmic development, software frameworks, and integration with existing computing infrastructure are crucial for widespread adoption. This paper explores the fundamentals of quantum computing, its recent breakthroughs, and its future potential in reshaping computational science. The study presents quantitative data on quantum computing investments, research trends, and real-world applications while addressing technical and practical challenges. The findings highlight the need for interdisci plinary collaboration between physicists, computer scientists, and engineers to overcome limi tations and unlock quantum computing’s full 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.
Quantum computing is an emerging form of computing that uses the rules of quantum physics to process information in ways that are far different from how computers do it now. Quantum computing uses quantum bits, or qubits, which can exist in superpositions of states and get entangled with each other, which is different from classical computing, which uses a bit that represents binary values (0 or 1). These characteristics allow quantum devices to execute parallel computations, enabling significant speedups for specific categories of problems considered intractable for classical machines. This chapter gives a thorough introduction to quantum computing, starting with a historical overview of its foundations in quantum physics and significant theoretical advancements. It analyzes fundamental ideas including superposition, entanglement, quantum interference, and the no-cloning theorem, explaining how these elements distinguish quantum computation from traditional approaches. The advancement of quantum hardware is examined through several architectures, trapped ions, superconducting circuits, photonics, and topological qubits, together with software frameworks like Qiskit, Cirq, and PennyLane, which support algorithm development and simulation. The chapter examines fundamental quantum algorithms, such as Grover’s algorithm for database search, Shor’s algorithm for integer factorization, and hybrid variational methods designed for noisy intermediate-scale quantum devices. The practical applications in domains including as encryption, optimization, machine learning, chemistry, and finance emphasize the real-world significance of quantum technology. Challenges like decoherence, noise, scalability, and algorithmic maturity are rigorously analyzed, followed by an anticipated discourse on future trajectories including fault-tolerant architectures, quantum networking, and the development of quantum cloud services. This chapter presents a fundamental introduction for students, researchers, and practitioners seeking to comprehend the essential principles, functionalities, and possibilities of quantum computing in the post–Moore’s law period.
This paper presents a comprehensive review of quantum computing and the technological foundations of quantum processors. Quantum computing represents a transformative computational paradigm that leverages the fundamental principles of quantum mechanics—such as superposition, entanglement, and quantum interference—to solve complex problems that are intractable for classical computers. At the core of this paradigm lies the quantum processor, a specialized hardware system designed to initialize, control, and measure quantum bits (qubits) through precise gate operations and coherent interactions. The survey examines the essential theoretical concepts underlying quantum computation and provides a structured comparison of leading quantum processor platforms. Prominent architectures, including superconducting circuits, trapped-ion systems, and photonic implementations, are analyzed in terms of their physical mechanisms, scalability, coherence properties, and engineering challenges. Each platform offers distinct advantages and faces specific technical limitations related to noise, error correction, and fabrication complexity. In addition to reviewing hardware developments, this study highlights key obstacles in advancing quantum processors toward large-scale, fault-tolerant systems. Issues such as qubit stability, error mitigation, thermal management, and system integration are discussed to present a realistic perspective on current progress. Performance indicators including gate fidelity, coherence time, connectivity, and scalability are used to compare architectural approaches. The objective of this paper is to provide a clear and organized synthesis of recent advancements in quantum computing hardware, thereby offering researchers and practitioners a foundational reference to support continued innovation and development in this rapidly evolving field.
This article provides a comprehensive technical examination of quantum computing systems based on qubit technology, exploring their revolutionary potential to transform computational capabilities beyond classical limitations. Beginning with an analysis of the fundamental quantum mechanical principles—superposition, entanglement, and quantum interference—the article elucidates how these phenomena enable exponential computational advantages for specific problem domains. Various physical implementations of qubits are evaluated, including superconducting circuits, trapped ions, photonic systems, and theoretical topological approaches, with each platform presenting unique advantages and engineering challenges. It extends to practical applications across cryptography, optimization, and artificial intelligence, where quantum computing promises transformative capabilities. However, significant obstacles remain, including decoherence, high error rates, scalability limitations, and the ongoing development of practical quantum algorithms. Despite these challenges, the quantum computing landscape is evolving toward a hybrid paradigm where quantum and classical resources work in concert, with specialized quantum processors likely to deliver commercial value in specific domains before universal quantum computers become a reality.
Quantum computing is an emerging computational paradigm that uses the principles of quantum mechanics, including superposition, entanglement, and quantum interference, to process information in fundamentally different ways from classical computers. During the last decade, quantum computing has progressed from theoretical research toward experimental and cloud-accessible systems. Recent developments have focused on increasing the number and quality of qubits, reducing gate errors, developing quantum error correction, improving quantum software, and integrating quantum processors with classical high-performance computing systems. Despite significant progress, practical large-scale quantum computing remains challenging because quantum states are extremely sensitive to environmental disturbances, resulting in decoherence and computational errors. Other challenges include limited qubit coherence time, gate fidelity, scalability, cryogenic requirements, quantum error correction overhead, hardware connectivity, programming complexity, and the difficulty of demonstrating useful quantum advantage for real-world applications. Recent developments from major research organizations indicate a shift from simply increasing physical qubit counts toward building reliable logical qubits and fault-tolerant quantum systems. Google has reported progress toward error-corrected logical qubits, while IBM\\\'s roadmap focuses on increasingly complex processors, modular architectures, quantum-classical integration, and fault-tolerant systems. Microsoft is pursuing a topological-qubit approach aimed at scalable quantum computing. This paper reviews the current state of quantum computing, examines major technological and scientific challenges, analyzes current research directions, and identifies future opportunities in drug discovery, materials science, optimization, cryptography, artificial intelligence, finance, and scientific simulation. The study concludes that the most realistic future is likely to involve quantum-centric computing, in which quantum processors work together with classical CPUs, GPUs, high-performance computing systems, and quantum networks rather than completely replacing classical computers.