Quantum computing is being increasingly adopted for solving classically intractable problems across various domains. However, the availability of accessible and scalable software frameworks remains essential for practical experimentation and adoption. IBM's open-source quantum computing toolkit 'Qiskit' has become a key player in this space by offering tools for circuit design, simulation, hardware execution, and domain-specific applications. This survey provides a systematic review of how Qiskit has evolved and what it has contributed to several critical fields including cryptography and cybersecurity, image and signal processing, climate science and energy applications, and finance. We show how Qiskit facilitates hybrid classical-quantum workflows and enables the deployment of algorithms on physical quantum hardware through error mitigation and modular integration approaches. Our exploration covers several key applications, including quantum key distribution, climate simulation, and quantum-enhanced portfolio optimization, while providing practical insights into their implementation. This work also covers the framework's technical structure and current limitations associated with scalability and reproducibility. By bringing together developments that have been scattered across different areas, this work serves as a reference point for researchers and practitioners who want to understand or contribute to Qiskit-enabled quantum computing.
Quantum computing is being increasingly adopted for solving classically intractable problems across various domains. However, the availability of accessible and scalable software frameworks remains essential for practical experimentation and adoption. IBM's open-source quantum computing toolkit 'Qiskit' has become a key player in this space by offering tools for circuit design, simulation, hardware execution, and domain-specific applications. This survey provides a systematic review of how Qiskit has evolved and what it has contributed to several critical fields including cryptography and cybersecurity, image and signal processing, climate science and energy applications, and finance. We show how Qiskit facilitates hybrid classical-quantum workflows and enables the deployment of algorithms on physical quantum hardware through error mitigation and modular integration approaches. Our exploration covers several key applications, including quantum key distribution, climate simulation, and quantum-enhanced portfolio optimization, while providing practical insights into their implementation. This work also covers the framework's technical structure and current limitations associated with scalability and reproducibility. By bringing together developments that have been scattered across different areas, this work serves as a reference point for researchers and practitioners who want to understand or contribute to Qiskit-enabled quantum computing.
Quantum computing, built on the principles of quantum mechanics, has emerged as a transformative paradigm with the potential to revolutionize computational capabilities, enabling solutions to problems that are currently intractable for classical systems. Quantum computing integrates hardware, architectures, and software for improved applications while utilizing superposition, entanglement, and interference to promote materials science, cryptography, optimization, and artificial intelligence. Sections introduce and describe the model components needed in order to create quantum processors, including qubits, quantum gates and control electronics, as well as a review of new developments in quantum software frameworks and programming tools. Notable examples, such as Shor’s algorithm for cryptographic caveat, Grover’s algorithm for inefficient search processes, and quantum-enhanced optimization, illuminate the position of quantum computing as a means of tackling tough problems that span disciplines, such as pharmaceuticals, predictors of climate, and finance modeling. However, the field continues to face significant challenges, like limited scalability, quantum gate error rates, qubit coherence times, and difficulties integrating with classical systems. Future efforts will focus on developing hybrid quantum-classical systems, scalable architectures, and industry- specific applications to transition quantum computing from exploration to practical deployment. This review provides a comprehensive analysis of quantum computing, covering its potential, trends, and challenges, while exploring hardware-software integration, classicalquantum comparisons, and insights to guide research and industry in unlocking its revolutionary possibility.
We describe Qiskit, a software development kit for quantum information science. We discuss the key design decisions that have shaped its development, and examine the software architecture and its core components. We demonstrate an end-to-end workflow for solving a problem in condensed matter physics on a quantum computer that serves to highlight some of Qiskit's capabilities, for example the representation and optimization of circuits at various abstraction levels, its scalability and retargetability to new gates, and the use of quantum-classical computations via dynamic circuits. Lastly, we discuss some of the ecosystem of tools and plugins that extend Qiskit for various tasks, and the future ahead.
Quantum Computing (QC) has evolved into a computational model that can potentially solve problems of classes that cannot be solved by classical systems. This review gives a discussion on the innovation of software frameworks, experimental platforms and quantum computing tools used to compute quantum algorithm development, simulation and implementation. It concentrates on widely used programming environments, which compile abstract quantum models into practical workflows, including Cirq, TensorFlow Quantum, ProjectQ and Qiskit. The paper, besides describing the long-lasting problems related to scalability, error correction, and hardware reliability, also considers the architecture of the modern quantum computing platforms. Finally, new advances are outlined, such as quantum cloud service and the integration of artificial intelligence and quantum computing, and their role in the development of QC to viable and lucrative deployments. All in all, the discussion suggests that even now quantum computing still needs to be taken to the next level in terms of hardware infrastructure and software development that would allow progressing to the next stage of quantum technology testing and implementation.
Quantum computing is redefining computational boundaries, offering solutions to complex problems that classical computing struggles to address. A deep understanding of quantum principles, algorithms, and practical tools is essential for professionals and researchers navigating this transformative technology. Quantum computing is no longer a distant dream—it is rapidly becoming a cornerstone of modern innovation. This book provides a structured introduction to quantum computing, covering foundational principles such as superposition and entanglement, quantum gates, and circuits. Readers will gain hands-on experience with Qiskit and IBM Q, explore quantum error correction, and understand the real-world applications of quantum computing across industries. Alongside theoretical concepts, readers are introduced to hands-on quantum programming using Qiskit and IBM Q. The book also addresses practical challenges like quantum error correction and showcases real-world applications across industries, including cybersecurity, artificial intelligence, and material science. By the end of this book, readers will have a solid grasp of quantum computing fundamentals and hands-on experience with practical tools. Equipped with both conceptual understanding and hands-on experience, readers will be prepared to contribute meaningfully to research, solve industry-relevant problems, and engage confidently with the rapidly evolving field of quantum computing. --
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.
The term Quantum Computing(QC) has emerged with lots of promises specifically with respect to the hard and complex computational problems in recent years.Quantum computing already shown its supreme capability and achieved several milestones by generating solutions in various domains such as material science, bio-science, finance etc.A systematic survey is conducted over the a few but well-practiced quantum platforms for quantum application and research in this paper.The characteristics, the technique of the simulation at various level of the quantum platform like IBM Qiskit, Google Cirq etc. are discussed with respect to current scenario of advantages and disadvantages.This paper also tried to explain the applicability as well as the present day challenges of different quantum tools.