Entanglement is generally accepted as the resource that allows quantum systems to be more efficient in performing classical computation, but the practical utility of entanglement in a variety of quantum information-processing processes is inadequately comprehended. The following paper examines the operational importance, constraints, and trade-offs of entanglement in quantum algorithms, communication protocols, error-correction frameworks and complexity-theoretic settings. Though entanglement (between two or more systems) is a requirement of uniquely quantum behaviours: teleportation, superdense coding, and non-classical correlations, we have found that a larger entanglement does not necessarily improve algorithmic performance. We find through analytical modelling, small-circuit entropy analysis, and comparative analysis of representative use-cases, an optimum entanglement zone, at which performance gains cease to improve with noise, gate infidelity, circuit-depth and hardware decoherence. It has also been found that moderate, noise-resilient entanglement is most useful in algorithmic tasks, structural connectivity is essential in communication tasks, and that quantum error-correction needs highly stable, topology-optimised entangled states rather than maximally entangled states. In addition, resource-theory views indicate conflicts between entanglement entropy, gate cost and classical stimulability thresholds and point to the importance of designing entanglement-efficiently. The paper identifies multiparty entanglement as the bottleneck to scalable quantum advantage, especially with the constraints in NISQ era. Comprehensively, this paper has provided a single analytical framework explaining when, where and how entanglement has real computational benefit. The analysis argues the development of algorithms and hardware in a so-called just-enough entanglement paradigm, to optimise fidelity, limit the effects of noise, and have scalable quantum architectures.
Entanglement is generally accepted as the resource that allows quantum systems to be more efficient in performing classical computation, but the practical utility of entanglement in a variety of quantum information-processing processes is inadequately comprehended. The following paper examines the operational importance, constraints, and trade-offs of entanglement in quantum algorithms, communication protocols, error-correction frameworks and complexity-theoretic settings. Though entanglement (between two or more systems) is a requirement of uniquely quantum behaviours: teleportation, superdense coding, and non-classical correlations, we have found that a larger entanglement does not necessarily improve algorithmic performance. We find through analytical modelling, small-circuit entropy analysis, and comparative analysis of representative use-cases, an optimum entanglement zone, at which performance gains cease to improve with noise, gate infidelity, circuit-depth and hardware decoherence. It has also been found that moderate, noise-resilient entanglement is most useful in algorithmic tasks, structural connectivity is essential in communication tasks, and that quantum error-correction needs highly stable, topology-optimised entangled states rather than maximally entangled states. In addition, resource-theory views indicate conflicts between entanglement entropy, gate cost and classical stimulability thresholds and point to the importance of designing entanglement-efficiently. The paper identifies multiparty entanglement as the bottleneck to scalable quantum advantage, especially with the constraints in NISQ era. Comprehensively, this paper has provided a single analytical framework explaining when, where and how entanglement has real computational benefit. The analysis argues the development of algorithms and hardware in a so-called just-enough entanglement paradigm, to optimise fidelity, limit the effects of noise, and have scalable quantum architectures.
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 phenomena such as superposition and entanglement imbue quantum systems with information processing power in excess of their classical counterparts. These properties of quantum states are, however, highly fragile. As we enter the era of noisy intermediate-scale quantum (NISQ) devices, this vulnerability to noise is a major hurdle to the experimental realisation of quantum technologies. In this thesis we explore the role of noise in quantum information processing from two different perspectives. In Part I we consider noise from the perspective of quantum error correcting codes. Error correcting codes are often analysed with respect to simplified toy models of noise, such as iid depolarising noise. We consider generalising these techniques for analysing codes under more realistic noise models, including features such as biased or correlated errors. We also consider designing customised codes which not only take into account and exploit features of the underlying physical noise. Considering such tailored codes will be of particular importance for NISQ applications in which finite-size effects can be significant. In Part II we apply tools from information theory to study the finite-resource effects which arise in the trade-offs between resource costs and error rates for certain quantum information processing tasks. We start by considering classical communication over quantum channels, providing a refined analysis of the trade-off between communication rate and error in the regime of a finite number of channel uses. We then extend these techniques to the problem of resource interconversion in theories such as quantum entanglement and quantum thermodynamics, studying finite-size effects which arise in resource-error trade-offs. By studying this effect in detail, we also show how detrimental finite-size effects in devices such as thermal engines may be greatly suppressed by carefully engineering the underlying resource interconversion processes.
The advent of noisy intermediate-scale quantum (NISQ) devices has not only opened up new possibilities to studying quantum computational benefits, but it has also presented new challenges associated with noise and short coherence times. Entanglement is one of the inherent resources that can be used to perform quantum computation and implement the performance and reliability of quantum algorithms. This paper looks at entanglement dynamics in NISQ machines subjected to natural noises. The study focuses on the problem of examining the role of various noise channels in the creation, dynamics, and decay of the entanglement during quantum operations, such as depolarizing, amplitude damping, and phase damping. The study is based on theoretical modeling and numerical simulations of multi-qubit quantum circuits to determine the stability of entangled states with respect to circuit depth and intensity of noise. Concurrence and von Neumann entropy are examples of entanglement measures that are used to characterize correlations between qubits as well as to trace the dynamics of these correlations across computational interactions. The results show that noise has major impacts on entanglement distribution, which tends to cause rapid decays in highly entangled states with increase in circuit complexity. The findings, however, also show that there are some circuit architectures and error-mitigation schemes that can somewhat sustain entanglement with moderate levels of noise. The paper also emphasizes the relevance of minimalizing sequence of gates, and reduced circuit depth in an effort to preserve useful entanglement in present-day quantum hardware. These observations help in understanding the behaviour of quantum correlations better in real world quantum devices and provide a way of developing more resilient quantum algorithms that would work on NISQ systems. In general, the article offers a systematic view on entanglement behavior in disordered quantum systems and highlights its value in the development of quantum computing applications in the near term.
Quantum error correction (QEC) represents the cornerstone technology for realizing fault-tolerant quantum computing, addressing the fundamental challenge of quantum state decoherence in noisy intermediate-scale quantum (NISQ) devices. This comprehensive review examines recent advances in QEC implementations from 2020-2024, spanning theoretical foundations to practical hardware demonstrations. We analyze the evolution from pioneering stabilizer codes to modern topological approaches, with particular emphasis on surface codes and low-density parity-check (LDPC) codes that have demonstrated below-threshold error correction in superconducting and trapped-ion systems. The paper provides a comparative analysis of classical and machine learning-based decoder algorithms, evaluating their performance through complexity analysis and threshold comparisons in real-time error correction scenarios. Through examination of breakthrough experiments by Google, IBM, Quantinuum, and emerging platforms, we assess the current state of fault-tolerant quantum computing and identify critical engineering challenges including correlated noise models, cryogenic control systems, and scalable logical gate implementations. Our quantitative analysis reveals that while significant progress has been made toward practical fault-tolerance thresholds, achieving the resource efficiency and reliability required for large-scale quantum computation remains challenging. We provide detailed resource estimates for key applications including Shor's algorithm and optimization problems, along with analysis of industry roadmaps toward thousand-logical-qubit systems. This review serves as a comprehensive guide for researchers and engineers working toward the next generation of fault-tolerant quantum computers, with particular focus on information-theoretic perspectives relevant to quantum information entropy and syndrome processing.
Quantum error correction (QEC) represents the cornerstone technology for realizing fault-tolerant quantum computing, addressing the fundamental challenge of quantum state decoherence in noisy intermediate-scale quantum (NISQ) devices. This comprehensive review examines recent advances in QEC implementations from 2020-2024, spanning theoretical foundations to practical hardware demonstrations. We analyze the evolution from pioneering stabilizer codes to modern topological approaches, with particular emphasis on surface codes and low-density parity-check (LDPC) codes that have demonstrated below-threshold error correction in superconducting and trapped-ion systems. The paper provides a comparative analysis of classical and machine learning-based decoder algorithms, evaluating their performance through complexity analysis and threshold comparisons in real-time error correction scenarios. Through examination of breakthrough experiments by Google, IBM, Quantinuum, and emerging platforms, we assess the current state of fault-tolerant quantum computing and identify critical engineering challenges including correlated noise models, cryogenic control systems, and scalable logical gate implementations. Our quantitative analysis reveals that while significant progress has been made toward practical fault-tolerance thresholds, achieving the resource efficiency and reliability required for large-scale quantum computation remains challenging. We provide detailed resource estimates for key applications including Shor's algorithm and optimization problems, along with analysis of industry roadmaps toward thousand-logical-qubit systems. This review serves as a comprehensive guide for researchers and engineers working toward the next generation of fault-tolerant quantum computers, with particular focus on information-theoretic perspectives relevant to quantum information entropy and syndrome processing.
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.