Abstract: The accelerated development of quantum computing is offering new paradigms on how computationally intensiveproblems can be solved, which are beyond the feasibility bound aries of classical systems. Optimal quantum system operationin resources has thus become a research challenge of particular concern especially when considering hybrid quantum-classicalinfrastructure and near-term Noisy Intermediate-Scale Quantum (NISQ) devices. This survey is an overview of recent studies inthe area of quantum resource management, including more than 40 academic articles published since 2015. This paper suggests a four-layer taxonomy, where hardware level (quantum resource, quantum memory, coherence time,and quantum memory), algorithm-level (quantum optimization algorithm: QAOA and VQE), system-level (scheduling, allocation,and hybrid cloud integration) and application-level (quantum resource, application domain) deployments are used to categorize quantum resource management mechanisms. Moreover, the survey reviews cross-sectoral uses of quantum computing inagriculture, healthcare, industrial optimization, and the systems of the public, which is a promising use of quantum-enableddecision support and large-scale optimization. In addition to a synthesis of the available studies, the paperreveals the major constraints associated with current methods such as difficulties in scalability, sensitivity to noise, and thelack of developed resource orchestration systems. On the basis of these observations, a research roadmap is suggested, whichrepresents how quantum resource management may evolve be yond NISQ-implementation-based approaches to scalable faulttolerant quantum infrastructures. The survey is expected to give a systematic view of the new quantum resource managementstrategies to the researchers and practitioners, and also display potential directions of future research.
Abstract: The accelerated development of quantum computing is offering new paradigms on how computationally intensiveproblems can be solved, which are beyond the feasibility bound aries of classical systems. Optimal quantum system operationin resources has thus become a research challenge of particular concern especially when considering hybrid quantum-classicalinfrastructure and near-term Noisy Intermediate-Scale Quantum (NISQ) devices. This survey is an overview of recent studies inthe area of quantum resource management, including more than 40 academic articles published since 2015. This paper suggests a four-layer taxonomy, where hardware level (quantum resource, quantum memory, coherence time,and quantum memory), algorithm-level (quantum optimization algorithm: QAOA and VQE), system-level (scheduling, allocation,and hybrid cloud integration) and application-level (quantum resource, application domain) deployments are used to categorize quantum resource management mechanisms. Moreover, the survey reviews cross-sectoral uses of quantum computing inagriculture, healthcare, industrial optimization, and the systems of the public, which is a promising use of quantum-enableddecision support and large-scale optimization. In addition to a synthesis of the available studies, the paperreveals the major constraints associated with current methods such as difficulties in scalability, sensitivity to noise, and thelack of developed resource orchestration systems. On the basis of these observations, a research roadmap is suggested, whichrepresents how quantum resource management may evolve be yond NISQ-implementation-based approaches to scalable faulttolerant quantum infrastructures. The survey is expected to give a systematic view of the new quantum resource managementstrategies to the researchers and practitioners, and also display potential directions of future research.
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.
Abstract Quantum computing (QC) is an emerging paradigm with the potential to offer significant computational advantage over conventional classical computing by exploiting quantum‐mechanical principles such as entanglement and superposition. It is anticipated that this computational advantage of QC will help to solve many complex and computationally intractable problems in several application domains such as drug design, data science, clean energy, finance, industrial chemical development, secure communications, and quantum chemistry. In recent years, tremendous progress in both quantum hardware development and quantum software/algorithm has brought QC much closer to reality. Indeed, the demonstration of quantum supremacy marks a significant milestone in the Noisy Intermediate Scale Quantum (NISQ) era—the next logical step being the quantum advantage whereby quantum computers solve a real‐world problem much more efficiently than classical computing. As the quantum devices are expected to steadily scale up in the next few years, quantum decoherence and qubit interconnectivity are two of the major challenges to achieve quantum advantage in the NISQ era. QC is a highly topical and fast‐moving field of research with significant ongoing progress in all facets. A systematic review of the existing literature on QC will be invaluable to understand the state‐of‐the‐art of this emerging field and identify open challenges for the QC community to address in the coming years. This article presents a comprehensive review of QC literature and proposes taxonomy of QC. The proposed taxonomy is used to map various related studies to identify the research gaps. A detailed overview of quantum software tools and technologies, post‐quantum cryptography, and quantum computer hardware development captures the current state‐of‐the‐art in the respective areas. The article identifies and highlights various open challenges and promising future directions for research and innovation in QC.
Quantum computers represent a radical technological breakthrough in information processing by leveraging the principles of quantum mechanics to solve highly complex problems beyond the reach of classical systems. However, in the current NISQ era (noisy intermediate-scale quantum devices), the available hardware presents several limitations, such as a limited number of qubits, high error rates, and short coherence times. Efficient management of quantum resources, both physical and logical, is especially relevant in the design and deployment of quantum algorithms. In this paper, we analyze the role of resources in current uses of NISQ devices, identifying their relevance and implications for quantum software engineering. With this contribution, we aim to strengthen the field of Quantum Resource Estimation (QRE) and move toward scalable and reliable quantum software development
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.
This paper presents a qualitative analysis of hybrid quantum-classical computing approaches aimed at solving complex optimization problems using near-term quantum processors. Hybrid algorithms leverage the strengths of quantum and classical computing to tackle computationally intensive tasks often constrained by current quantum hardware limitations. Through an extensive literature review and synthesis of recent empirical studies, we benchmark various hybrid algorithms, such as the Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE), focusing on their performance, scalability, and practical applicability on noisy intermediate-scale quantum (NISQ) devices. The study highlights the advantages, challenges, and future prospects of integrating hybrid quantum-classical computation in optimization domains, providing a comprehensive framework and qualitative insights into benchmarking methodologies and critical performance metrics.
Quantum computers represent a radical technological advancement in the way information is processed by using the principles of quantum mechanics to solve very complex problems that exceed the capabilities of classical systems. However, in the current NISQ era (Noisy Intermediate-Scale Quantum devices), the available hardware presents several limitations, such as a limited number of qubits, high error rates, and reduced coherence times. Efficient management of quantum resources, both physical (qubits, error rates, connectivity) and logical (quantum gates, algorithms, error correction), becomes particularly relevant in the design and deployment of quantum algorithms. In this work, we analyze the role of resources in the various uses of NISQ devices today, identifying their relevance and implications for software engineering focused on the use of quantum computers. We propose a vision for runtime-aware quantum software development, identifying key challenges to its realization, such as limited introspection capabilities and temporal constraints in current platforms. As a proof of concept, we introduce Qonscious, a prototype framework that enables conditional execution of quantum programs based on dynamic resource evaluation. With this contribution, we aim to strengthen the field of Quantum Resource Estimation (QRE) and move towards the development of scalable, reliable, and resource-aware quantum software.