Summary Computing methodologies with rapid proliferation and need for greater concurrency have evolved over the years from parallel and distributed computing to latest technologies like cloud computing. A paradigm shift in computing methodologies has been witnessed in the form of exponential speed up, optimization of problem solutions, and solvability of few classically unsolvable problems with emergence of quantum computing. The study in this article presents observations on executing basic quantum operations that play a vital role in quantum computing. The results and analysis demonstrate how parameters of time, deviation, and shots have a significant effect on outcomes of quantum circuits executed which can be a reference point for the community in general engaged in designing of quantum circuits, protocols, algorithms, and quantum hardware to leverage quantum characteristics and properties. The study by means of experimental use cases provides a rigorous review of qubit behavior, standard, and entangled qubit measurement with practical results over a simulator and real quantum hardware for variable qubit configurations using a selected platform of IBM Q Experience. With the paradigm shift from classical to quantum computing, this article provides basic but very vital observations which can be brought to use by anyone intending to build quantum applications.
Summary Computing methodologies with rapid proliferation and need for greater concurrency have evolved over the years from parallel and distributed computing to latest technologies like cloud computing. A paradigm shift in computing methodologies has been witnessed in the form of exponential speed up, optimization of problem solutions, and solvability of few classically unsolvable problems with emergence of quantum computing. The study in this article presents observations on executing basic quantum operations that play a vital role in quantum computing. The results and analysis demonstrate how parameters of time, deviation, and shots have a significant effect on outcomes of quantum circuits executed which can be a reference point for the community in general engaged in designing of quantum circuits, protocols, algorithms, and quantum hardware to leverage quantum characteristics and properties. The study by means of experimental use cases provides a rigorous review of qubit behavior, standard, and entangled qubit measurement with practical results over a simulator and real quantum hardware for variable qubit configurations using a selected platform of IBM Q Experience. With the paradigm shift from classical to quantum computing, this article provides basic but very vital observations which can be brought to use by anyone intending to build quantum applications.
Quantum Computing technologies with rapid proliferation have achieved important milestones in recent years. Quantum concept of entanglement has emerged rapidly with its application domains widely expanding. Here, in this study we elaborate on relationship of classical and quantum technologies focusing on the entanglement property. This study reviews how entanglement influences various quantum applications and elaborates on qubit behaviour to compare entanglement realization on real quantum hardware prototypes and simulated noise model environments in IBM Q Experience which could assist future studies in overcoming associated challenges in entanglement utility in practical implementations of upcoming applications.
Abstract Quantum computing leverages fundamental quantum-mechanical phenomena—most notably superposition, entanglement, and quantum tunneling—to perform computations in ways that differ fundamentally from classical digital processing. Compared to conventional computing architectures, quantum systems promise significant reductions in computational time and potentially lower energy consumption for specific classes of problems. These advantages, combined with advances in nanoscale engineering and the slowing of traditional hardware scaling trends such as Moore’s Law, have led to substantial commercial and national-security-driven investments in quantum technologies over the past decade. A major milestone was reached when Google announced the experimental demonstration of “quantum supremacy,” showing that a quantum processor could execute a specific computational task infeasible for a classical supercomputer within practical time constraints. Although the demonstrated problem had no immediate practical application, this achievement marked a crucial step toward the development of quantum systems capable of delivering real-world computational advantages and provided a more precise technical framing of the concept of quantum advantage. Despite the fact that the mathematical foundations, programming models, and principal quantum algorithms were established in the 1990s, quantum computing remained largely confined to a specialized research community for decades. Today, the increasing accessibility of cloud-based quantum hardware calls for a broader dissemination of implementation-oriented knowledge among researchers and practitioners in computer science, software engineering, and related fields. The quantum programming paradigm differs fundamentally from classical programming models and often relies on physical and algebraic notations that may pose barriers to traditionally trained computer scientists. This series of articles provides a self-contained and concise introduction to quantum computing and its core algorithms, with a strong emphasis on practical implementation. Given that real quantum processors—such as those accessible via the IBM quantum ecosystem—are now available as cloud services, experimental validation can be conducted both on simulators and on actual hardware for small-scale inputs. While other platforms such as Rigetti and IonQ also offer cloud-based quantum computing services, this review focuses exclusively on the IBM quantum computing ecosystem.
Quantum Computing (QC) refers to an emerging paradigm that inherits and builds with the concepts and phenomena of Quantum Mechanic (QM) with the significant potential to unlock a remarkable opportunity to solve complex and computationally intractable problems that scientists could not tackle previously. In recent years, tremendous efforts and progress in QC mark a significant milestone in solving real-world problems much more efficiently than classical computing technology. While considerable progress is being made to move quantum computing in recent years, significant research efforts need to be devoted to move this domain from an idea to a working paradigm. In this paper, we conduct a systematic survey and categorize papers, tools, frameworks, platforms that facilitate quantum computing and analyze them from an application and Quantum Computing perspective. We present quantum Computing Layers, Characteristics of Quantum Computer platforms, Circuit Simulator, Open-source Tools- Cirq, TensorFlow Quantum, ProjectQ etc. that allow implementing quantum programs in Python using a powerful and intuitive syntax. Following that, we discuss the current essence, identify open challenges, and provide future research direction. We conclude that scores of frameworks, tools and platforms are emerged in the past few years, improvement of currently available facilities would exploit the research activities in the quantum research community.
Combinatorial optimization problems play a vital role in computer science and engineering, with applications in logistics, network design, finance, and resource allocation. However, many of these problems are NP-hard, making them computationally expensive to solve using classical optimization techniques, especially for large-scale instances. In recent years, quantum computing has emerged as a promising paradigm capable of addressing these limitations by leveraging quantum mechanical principles such as superposition and entanglement. This study investigates the application of quantum computing for solving combinatorial optimization problems, with a focus on algorithm design, complexity analysis, and real-world applicability. The research primarily employs the Quantum Approximate Optimization Algorithm (QAOA) and Grover's search algorithm to solve representative problems such as Max-Cut and the Knapsack problem. The performance of these algorithms is evaluated using key metrics including accuracy, execution time, and approximation ratio, and is compared with classical optimization techniques. The results demonstrate that QAOA is capable of generating near-optimal solutions, achieving accuracy levels of up to 93% for small problem instances. However, the study also highlights the challenges associated with current quantum systems, including noise, limited qubit availability, and increased execution time. Comparative analysis reveals that while classical methods remain more efficient for small-scale problems, quantum algorithms show significant potential for scalability and handling complex optimization tasks. The findings suggest that hybrid quantum-classical approaches offer a practical solution in the current Noisy Intermediate-Scale Quantum (NISQ) era. The study concludes that although quantum computing is still evolving, it holds strong potential for transforming combinatorial optimization in the future.
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.
The rapid growth of computational technology has given rise to new paradigms that aim to redefine the limits of traditional computing. Of these, quantum computing is a paradigm change that applies to the principles of quantum mechanics, such as superposition and entanglement, to compute at much higher rates than classical computing is capable of. This paper covers the evolution of computing paradigms, focusing on quantum computing. It examines critically the principles, paradigm of programming and performance elements that differentiate quantum computing from mainstream computing. It also investigates scalability challenges and technologies that are advancing quantum computing technology. The work further investigates disruptive applications of quantum computing in key industries like artificial intelligence, cryptography, medicine modeling, and financial modeling. Despite current constraints in terms of hardware limitations and error correction, the potential for quantum computing is vast in redesigning problem-solving approaches across industries. This consideration emphasizes the need for continued research and development to tap into the full potential of quantum computing in solving complex computational problems.