The current stage of quantum computing development is defined by Noisy Intermediate-Scale Quantum (NISQ) devices, which operate with a moderate number of qubits that are inherently susceptible to noise and decoherence. Despite the absence of full error correction, NISQ systems have demonstrated the ability to perform computational tasks that challenge classical simulation under certain conditions. This paper explores the concept of quantum advantage in the NISQ era by analysing prominent algorithmic approaches, benchmarking methodologies, and the practical constraints imposed by contemporary hardware. Hybrid quantum–classical algorithms, sampling-based experiments, and performance metrics are examined to assess the extent to which near-term quantum devices can outperform classical systems. The study finds that while progress toward quantum advantage is evident, significant technical and algorithmic limitations prevent its widespread realization. The paper concludes by identifying key research directions required to bridge the gap between experimental demonstrations and practical quantum computing applications.
The current stage of quantum computing development is defined by Noisy Intermediate-Scale Quantum (NISQ) devices, which operate with a moderate number of qubits that are inherently susceptible to noise and decoherence. Despite the absence of full error correction, NISQ systems have demonstrated the ability to perform computational tasks that challenge classical simulation under certain conditions. This paper explores the concept of quantum advantage in the NISQ era by analysing prominent algorithmic approaches, benchmarking methodologies, and the practical constraints imposed by contemporary hardware. Hybrid quantum–classical algorithms, sampling-based experiments, and performance metrics are examined to assess the extent to which near-term quantum devices can outperform classical systems. The study finds that while progress toward quantum advantage is evident, significant technical and algorithmic limitations prevent its widespread realization. The paper concludes by identifying key research directions required to bridge the gap between experimental demonstrations and practical quantum computing applications.
The transition from theoretical quantum mechanics to practical quantum computing represents one of the most significant technological developments of the 21st century. This comprehensive survey examines the evolution from foundational quantum mechanical principles to contemporary quantum computing implementations. We analyze how fundamental quantum phenomena—superposition, entanglement, and unitary evolution—are systematically harnessed to achieve computational advantages over classical systems. The paper provides detailed analysis of key quantum algorithms including Deutsch-Jozsa, Grover's search, and Shor's factorization algorithm, emphasizing their theoretical foundations and computational complexity advantages. We present a comparative evaluation of current hardware platforms including superconducting qubits, trapped ions, photonic systems, and semiconductor quantum dots, analyzing their respective merits, limitations, and scalability prospects. The challenges of quantum error correction, decoherence mitigation, and the path toward fault-tolerant quantum computation are critically examined within the context of the current Noisy Intermediate-Scale Quantum (NISQ) era. This interdisciplinary synthesis bridges theoretical quantum mechanics and practical quantum computing, highlighting the remarkable progress achieved and the substantial challenges that remain in realizing the full potential of quantum computation.
Quantum computing has the potential to solve complex problems that are outside the scope of classical systems, making it a standard shifter. Current devices, which are considered as Noisy Intermediate-Scale Quantum (NISQ) systems, are insufficient in their practical utility due to hardware limitations, error rates, and qubit counts. The dynamic growths wanted to exceed the NISQ era and grow scalable quantum algorithms and architectures that can achieve useful quantum benefit in real-world applications are reviewed in this research. This research gives a comprehensive examination of the complications in scaling quantum systems, adjacent resource efficacy, qubit connectivity, and error correction. In addition to connecting architectural innovations like modular quantum computing, error-resilient circuit design, and distributed quantum systems, the paper provides new hybrid quantum-classical algorithms planned to exploit performance in noisy environments. Results from benchmarking in arenas like machine learning, optimization, and quantum chemistry show how these methods can provide short-term quantum benefit. Lastly, this paper proposes a research roadmap for quantum computing that highlights the requirement of co-designing algorithms, software, and hardware. The goal of this effort is to straighten the progress of quantum systems that can resolve problems in the real world at scale by addressing the inadequacies of NISQ devices and emphasizing scalable solutions.
Quantum computing has emerged as a transformative paradigm capable of addressing computational challenges beyond the reach of classical systems.In the current era of Noisy Intermediate-Scale Quantum (NISQ) devices, practical quantum hardware is characterized by limited qubit counts, short coherence times, gate errors, and the absence of full error correction.Despite these constraints, significant progress has been made in developing quantum machine learning (QML) techniques tailored to operate on near-term quantum processors.This paper presents a comprehensive review of Quantum Machine Learning in the context of NISQ computing.We first outline the fundamental principles of quantum computing, including qubits, superposition, entanglement, and quantum parallelism, followed by a detailed discussion of the NISQ computing model and its hardware limitations.The review then examines hybrid quantum-classical learning frameworks, variational quantum algorithms, quantum data encoding strategies, and noise mitigation techniques that enable practical implementation on existing platforms.Emerging applications across healthcare, finance, cybersecurity, climate modeling, and materials science are also surveyed to highlight the real-world potential of QML.Furthermore, key challenges such as scalability, trainability issues, and algorithmic robustness are analyzed, along with current research efforts aimed at overcoming these barriers.The paper concludes by identifying promising future directions for advancing QML toward quantum advantage.This review aims to provide researchers and practitioners with a structured understanding of the capabilities, limitations, and opportunities of quantum machine learning in the NISQ era .
Quantum computing has transitioned from a theoretical curiosity to an emerging technological paradigm with demonstrable computational advantages in specific problem domains. This paper presents a comprehensive review of current advances in quantum computing hardware, software, and near-term applications within the Noisy Intermediate-Scale Quantum (NISQ) era. We examine the architectural evolution of leading quantum platforms, including superconducting, trapped-ion, and photonic systems, analyzing their qubit counts, gate fidelities, and coherence times. The paper evaluates key quantum algorithms Shor's factoring algorithm [1], Grover's search algorithm [2], the Variational Quantum Eigensolver (VQE) [3], and the Quantum Approximate Optimization Algorithm (QAOA) [4] in the context of their practical applicability to real-world problems. We further discuss the milestone of quantum computational advantage demonstrated by Google's Sycamore processor [5] and IBM's ambitious hardware roadmap toward fault-tolerant quantum computing [6]. Our analysis reveals that while universal fault-tolerant quantum computing remains a long-term objective, NISQ-era devices are already yielding valuable results in quantum chemistry simulation, combinatorial optimization, and quantum machine learning. We conclude by identifying the critical challenges—error correction, scalability, and algorithm design that must be addressed to realize the full potential of quantum computing.
The paper provides an exploration of quantum computing, emphasizing its potential and challenges. It begins with the fundamentals of quantum mechanics and differentiates between classical and quantum computing, focusing on concepts like qubits, superposition, and entanglement. The authors identify the existing limitations in practical quantum applications, particularly within the Noisy Intermediate-Scale Quantum (NISQ) era, where issues like noise in quantum circuits hinder scalability and reliability. The paper addresses the need for advancements in hardware, error correction techniques, and practical algorithms tailored for current technological constraints. Additionally, it discusses the implications of quantum computing on cryptography, underscoring the vulnerability of traditional cryptographic methods and the urgency for postquantum cryptographic solutions. The methodology involves literature review, expert consultations, and bibliometric analysis to gauge global research trends in quantum computing. The paper highlights crucial research gaps, particularly in the development of scalable quantum algorithms suitable for near-future applications.
Quantum computing has recently achieved experimental demonstrations of quantum advantage, marking a major milestone toward beyond-classical computation. However, translating these demonstrations into practical utility remains a central challenge. In this perspective, we argue that this transition is fundamentally limited by three interconnected gaps: the utility gap between benchmark tasks and real-world applications, the algorithmic gap between asymptotic quantum speedups and realistic performance, and the hardware gap imposed by noisy intermediate-scale devices. We further highlight that recent progress is beginning to narrow these gaps through the rapid maturation of quantum hardware, the emergence of resource-aware and practically grounded quantum algorithms, and the increasing integration of classical computation into the quantum workflow. Finally, we discuss potential early application domains for practical quantum advantage, while emphasizing that its ultimate realization will likely emerge from the co-evolution of hardware, algorithms, and application-driven discovery rather than any single breakthrough.