ABSTRACT Quantum computing exploits the principles of quantum mechanics to address computational problems that are intractable to classical systems. This study examines the evolution, architecture, and applications of the field, with a focus on foundational principles, hardware advancements, and algorithmic progress. Recent quantum processors, such as Google's Willow and IBM's Heron, represent significant advancements in qubit count and gate fidelity; however, they remain constrained by qubit instability, environmental noise, and limitations of current error correction techniques. Quantum algorithms, including Shor's, Grover's, and HHL algorithms, have demonstrated substantial speedups in cryptography, optimization, and machine learning. Nevertheless, the realization of this potential in real‐world problems encounters major bottlenecks related to low qubit counts and error correction. Applications span domains such as cryptography, drug discovery, precision medicine, financial modeling, and materials science, in which quantum computation offers potential breakthroughs. However, the development of practical quantum systems presents a substantial challenge. Key programming languages, such as Q#, Qiskit, and Cirq, facilitate algorithmic development and deployment; however, the efficiency of current quantum algorithms is limited by hardware constraints. The future of quantum computing lies in interdisciplinary collaboration, the development of resource‐efficient error‐correction techniques, and continued hardware development. This study underscores the potential of quantum computing, while emphasizing the research and development required to fully harness its capabilities to address major scientific and technological challenges.
ABSTRACT Quantum computing exploits the principles of quantum mechanics to address computational problems that are intractable to classical systems. This study examines the evolution, architecture, and applications of the field, with a focus on foundational principles, hardware advancements, and algorithmic progress. Recent quantum processors, such as Google's Willow and IBM's Heron, represent significant advancements in qubit count and gate fidelity; however, they remain constrained by qubit instability, environmental noise, and limitations of current error correction techniques. Quantum algorithms, including Shor's, Grover's, and HHL algorithms, have demonstrated substantial speedups in cryptography, optimization, and machine learning. Nevertheless, the realization of this potential in real‐world problems encounters major bottlenecks related to low qubit counts and error correction. Applications span domains such as cryptography, drug discovery, precision medicine, financial modeling, and materials science, in which quantum computation offers potential breakthroughs. However, the development of practical quantum systems presents a substantial challenge. Key programming languages, such as Q#, Qiskit, and Cirq, facilitate algorithmic development and deployment; however, the efficiency of current quantum algorithms is limited by hardware constraints. The future of quantum computing lies in interdisciplinary collaboration, the development of resource‐efficient error‐correction techniques, and continued hardware development. This study underscores the potential of quantum computing, while emphasizing the research and development required to fully harness its capabilities to address major scientific and technological challenges.
Quantum computing represents a new paradigm in computation that exploits the principles of quantum mechanics to solve complex problems beyond the practical capabilities of classical computers. By utilizing qubits, superposition, entanglement, and quantum interference, quantum computers have the potential to revolutionize cryptography, optimization, drug discovery, artificial intelligence, financial modeling, and materials science. Although remarkable progress has been achieved in quantum hardware and algorithms, significant challenges remain, including qubit stability, error correction, scalability, high implementation costs, and limited practical applications. This paper presents the fundamental principles of quantum computing, explores its major applications, examines current limitations, and discusses future research directions.
Quantum computing leverages quantum mechanical concepts, including superposition, entanglement, and interference, to solve problems that traditional computers cannot handle efficiently. This study presents a comprehensive overview of the field, covering both the theoretical foundations and the latest developments in quantum hardware and algorithms. This study covers fundamental elements, qubits, gates, and circuits while emphasizing quantum parallelism as a distinctive feature. To illustrate efficiency gains, we explore notable algorithms like Shor’s factorization procedure and Grover’s search algorithm. Furthermore, we analyze recent advancements in quantum hardware, focusing on superconducting qubits, trapped ions, and photonic quantum computers, with an emphasis on implementations by IBM, Google, and other industry leaders. The discussion also addresses major challenges such as decoherence, noise, and the need for quantum error correction to achieve fault tolerance. Finally, we explore emerging applications in cryptography, optimization, quantum machine learning, and material simulation, and identify critical open problems that must be addressed to realize scalable, universal quantum computing. Our review underscores the transformative potential of quantum technologies across science and industry.
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 advanced computational paradigm that leverages principles of quantum mechanics, including superposition, entanglement, and interference, to process information more efficiently than classical systems [1][2]. Unlike classical bits, qubits can exist in multiple states simultaneously, enabling exponential computational capabilities for complex problem-solving. Since its theoretical foundations proposed by Richard Feynman and further developed by David Deutsch, quantum computing has progressed into an active research and industrial domain [2][3]. This paper presents an overview of the core concepts of quantum computation, technological evolution, and key algorithms such as Shor’s and Grover’s, which demonstrate significant computational advantages [9][10]. It also explores major application areas including cryptography, healthcare, artificial intelligence, and optimization, while addressing critical challenges such as decoherence, error correction, and scalability [5][6]. The study highlights future prospects, emphasizing the potential of quantum computing to transform scientific and industrial domains [12][15].
Quantum computing represents a transformative shift in computational technology, leveraging the principles of quantum mechanics to process information in ways that surpass the limits of classical systems.By exploiting qubits, superposition, entanglement, and quantum parallelism, quantum computers offer the potential to solve certain classes of problems-such as optimization, cryptography, molecular simulation, and complex machine learning tasks-significantly faster than classical algorithms.With rapid advancements in quantum hardware and the emergence of Noisy Intermediate-Scale Quantum (NISQ) devices, quantum computing has evolved from a theoretical concept to a practical field with growing industrial and scientific relevance.Although current systems face challenges related to error rates, decoherence, and scalability, ongoing developments in quantum error correction, hybrid quantum-classical algorithms, and software frameworks like Qiskit, Cirq, and PennyLane continue to push the field forward.This paper provides an introductory overview of quantum computing, outlining its foundational principles, current technological landscape, key algorithms, and future research directions.The goal is to offer a clear understanding of how quantum computing is poised to reshape the future of computation.
ABSTRACT Quantum computing represents a fundamental shift in the way computational problems are approached and solved. Unlike classical computing systems that rely on binary bits to process information, quantum computing utilizes quantum bits, or qubits, which exploit principles of quantum mechanics such as superposition and entanglement. This transition from classical bits to qubits has opened new possibilities for solving complex problems that are computationally infeasible for traditional systems. This paper explores the evolution of computing paradigms from classical architectures to quantum-based models, highlighting the theoretical foundations and technological advancements that have driven this transformation. It discusses the core concepts of quantum computing, including qubit representation, quantum gates, and computational models, along with an overview of current quantum hardware architectures. Furthermore, the paper examines key application areas such as cryptography, optimization, artificial intelligence, and scientific simulations, where quantum computing demonstrates significant potential. Despite its promise, quantum computing faces substantial challenges related to hardware stability, error correction, scalability, and resource requirements, which are also analyzed. Finally, the paper outlines future research directions and the potential impact of quantum computing on next-generation information processing systems, emphasizing its role in redefining the boundaries of modern computation. Keywords : Quantum Computing, Qubits, Quantum Algorithms, Quantum Applications, Future Computing Technologies