One of the key non-classical resources involved in the promise of quantum computing is quantum entanglement. It allows for correlations between qubits that are not possible by independent local states, thus it can be used for computational speed-up, quantum simulation, quantum error correction, quantum machine learning, and secure quantum communication. This paper explores the role of entanglement in quantum computing, and looks at its applications with a secondary-data based research design. This study will evaluate the progress of entanglement from a fundamental physical phenomenon to an engineering resource for processors, algorithms, and fault-tolerant architectures from peer-reviewed literature, institutional reports and public research data from 2018 to 2025. The paper is divided into five subheadings in the introduction, four subheadings in the literature review, a research methodology, and a results section with five secondary-data tables, and a conclusion. The findings reveal a significant acceleration in noisy intermediate-scale quantum devices between 2018 and 2025, along with efforts to prove quantum computational advantage, grow of variational algorithms and increased advancement toward logical qubits and quantum error correction. The evidence also shows that there are important limitations for practical quantum benefit, including decoherence, errors in quantum gates, difficulties loading data, benchmarking arguments, and the high cost of overhead error correction in fault-tolerant quantum computing. The authors conclude that entanglement is not just a theoretical property of quantum physics, but the resource that is needed to link hardware and software with applications. In the future, fidelity of entangling gates, scalable qubit connectivity, error-mitigated algorithms, and fault-tolerant logical qubit designs will all be important.
One of the key non-classical resources involved in the promise of quantum computing is quantum entanglement. It allows for correlations between qubits that are not possible by independent local states, thus it can be used for computational speed-up, quantum simulation, quantum error correction, quantum machine learning, and secure quantum communication. This paper explores the role of entanglement in quantum computing, and looks at its applications with a secondary-data based research design. This study will evaluate the progress of entanglement from a fundamental physical phenomenon to an engineering resource for processors, algorithms, and fault-tolerant architectures from peer-reviewed literature, institutional reports and public research data from 2018 to 2025. The paper is divided into five subheadings in the introduction, four subheadings in the literature review, a research methodology, and a results section with five secondary-data tables, and a conclusion. The findings reveal a significant acceleration in noisy intermediate-scale quantum devices between 2018 and 2025, along with efforts to prove quantum computational advantage, grow of variational algorithms and increased advancement toward logical qubits and quantum error correction. The evidence also shows that there are important limitations for practical quantum benefit, including decoherence, errors in quantum gates, difficulties loading data, benchmarking arguments, and the high cost of overhead error correction in fault-tolerant quantum computing. The authors conclude that entanglement is not just a theoretical property of quantum physics, but the resource that is needed to link hardware and software with applications. In the future, fidelity of entangling gates, scalable qubit connectivity, error-mitigated algorithms, and fault-tolerant logical qubit designs will all be important.
Quantum computation exploits superposition, entanglement, interference, and measurement to process information.Research has moved from theoretical algorithms to functional devices where the performance is not only limited by qubit physics, but also by the control, compilation, error mitigation, and error correction.This review considers quantum computing on various levels of abstraction, including physical qubits, quantum algorithms, system architecture, fault-tolerance, and emerging applications.Study-specific evidence regarding scale, operational quality, connectivity, control and suppression of errors is compared across the different types of superconducting circuits, trapped ions, semiconductor spin qubits, neutral atoms and photonic processors.The review of the fundamental algorithms, noisy intermediate-scale quantum computing, variational and hybrid quantum-classical approaches, quantum simulation, quantum machine learning, and security implications is also discussed.Measurable progress has been demonstrated in published experiments for logical-qubit operation, below-threshold surface-code memory, programmable photonic sampling and platformspecific scaling.However, the reported metrics are not directly interchangeable, and practical advantage remains task-and baseline-dependent.Further progress will need to be accompanied by coordinated developments in hardware reliability, error correction, classical control, estimates of algorithmic resources, and clear classical method comparisons.
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 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.
Quantum computing represents a transformative paradigm that exploits the principles of quantum mechanics—superposition, entanglement, and quantum interference—to perform computations beyond the capabilities of classical computers. This review presents a comprehensive overview of the fundamental concepts underlying quantum computation, including qubits, quantum gates, quantum algorithms and measurement theory. It further examines major physical implementations of quantum computers, such as superconducting circuits, trapped ions, photonic systems, spin-based qubits, and topological approaches, highlighting their operational principles, advantages, and technological limitations. Key challenges hindering large-scale quantum computation—decoherence, error rates, qubit scalability, and fault tolerance—are critically discussed. Additionally, recent progress in quantum error correction, hybrid quantum–classical algorithms, and hardware optimization is summarized. The review concludes by outlining future research directions and potential applications of quantum computing in cryptography, materials science, optimization, and drug discovery. By integrating theoretical foundations with experimental advancements, this article aims to provide a clear and accessible reference for students, researchers, and scientists entering the rapidly evolving field of quantum computing.
Quantum computing, grounded in the principles of superposition and entanglement, has emerged as a revolutionary paradigm with the potential to outperform classical systems in specific computational tasks. This paper presents a comprehensive survey of the foundational concepts, algorithmic developments, hardware architectures, and programming ecosystems in quantum computing. We analyze the current progress in key application areas such as cryptography, machine learning, optimization, and quantum chemistry. Furthermore, we review recent advances between 2023 and 2025, including developments in fault-tolerant architectures and quantum error correction. The paper also discusses the significant technical challenges that hinder large-scale practical deployment, including qubit scalability, decoherence, and programming limitations. By synthesizing literature across disciplines, this review aims to provide a holistic understanding of the quantum computing landscape and outline critical directions for future research and integration with classical systems.
Quantum entanglement, where two or more particles become correlated in such a way that the state of one particle is dependent on the state of the other(s), regardless of the distance between them, is an important concept in quantum mechanics. Here, we explore the role of quantum entanglement in quantum computing, by looking at its basic principles, and the new levels of processing power and security it allows. We also discuss the latest advances, problems and opportunities of quantum entanglement in information processing technologies, such as quantum algorithms, quantum encryption and its effect on conventional computers.