Quantum computing is an upcoming paradigm that has been in the transformation process in data science in the light of quantum physics. On that account, the present chapter delivers in-depth details on how quantum phenomena like superposition and entanglement could be brought to bear on data-intensive problems that are beyond classical computational means. Basic concepts such as qubits, gates, and circuits are studied with an emphasis on parallelism and interference to speed up computations. They look at the major applications for data science with a focus on 32 promising quantum machine-learning techniques: quantum support vector machines and quantum neural networks that promise a quantum speed-up in classifying and regressing data sets. It also looks at quantum methods for clustering, dimensionality reduction, and combinatorial optimization, including quantum annealing and the quantum approximate optimization algorithm, to name just a few, with respect to speed and accuracy. Therefore, quantum simulation is strongly promoted for the simulation of complicated systems, which are of interest, for example, in chemistry, materials science, and drug discovery. Besides, some shelves are kept for an overview of the interdisciplinary approaches of quantum computing with big data analytics and also with cryptography, including quantum-safe encryption. The hardware scalability stands as the main challenge, while the prime concern is error correction. Other than these issues, advances in investigating and developing are schema concepts for unleashing the power of quantum computing to change data science.
Quantum computing is an upcoming paradigm that has been in the transformation process in data science in the light of quantum physics. On that account, the present chapter delivers in-depth details on how quantum phenomena like superposition and entanglement could be brought to bear on data-intensive problems that are beyond classical computational means. Basic concepts such as qubits, gates, and circuits are studied with an emphasis on parallelism and interference to speed up computations. They look at the major applications for data science with a focus on 32 promising quantum machine-learning techniques: quantum support vector machines and quantum neural networks that promise a quantum speed-up in classifying and regressing data sets. It also looks at quantum methods for clustering, dimensionality reduction, and combinatorial optimization, including quantum annealing and the quantum approximate optimization algorithm, to name just a few, with respect to speed and accuracy. Therefore, quantum simulation is strongly promoted for the simulation of complicated systems, which are of interest, for example, in chemistry, materials science, and drug discovery. Besides, some shelves are kept for an overview of the interdisciplinary approaches of quantum computing with big data analytics and also with cryptography, including quantum-safe encryption. The hardware scalability stands as the main challenge, while the prime concern is error correction. Other than these issues, advances in investigating and developing are schema concepts for unleashing the power of quantum computing to change data science.
Fast data and growing artificial intelligence have brought traditional computer infrastructures to their limits. They expose inherent limitations in processing speed, energy efficiency, and scalability. In the face of nearing physical barriers with Moore's Law and memory bottlenecks plaguing traditional von Neumann architectures, quantum computing offers a completely new way of leveraging principles such as superposition and entanglement to redefine computation possibilities. This review is concerned with addressing how quantum computing stands to do that foundational AI and big data challenges, from accelerating extremely complex optimization tasks to enabling very secure, real-time analytics. These topics include basic principles of quantum mechanics, the basis of qubits and quantum circuits; updates in quantum algorithms (such as Grover's search, quantum annealing); and hybrid classical-quantum systems that serve as pathways through technological gaps. The paper suggests major applications of transformative nature in some of the higher-impact domains: quantum-accelerated drug discovery for health, portfolio optimization in finance, and quantum-secured communication infrastructures to guard the IoT. Case studies present early successes on quantum machine learning models improving diagnostic accuracy and annealing-based traffic optimization for urban congestion. Decoherence, error-prone devices, as well as low software ecosystems are conspiring against the actual roadways to practical quantum advantages. Future progress is dependent on the innovative development of error-corrected qubits, scalable algorithms, as well as interdisciplinary collaboration. While universal quantum supremacy remains while unreached, this coming decade promises a series of developments toward applied, specific applications that will place quantum computing as an added force among the classical systems. This review tries to project both sides of the likely potential of the space, obstacles, and path, paving the way for the researcher to find their foot in the continually progressing quantum-AI terrain.
Quantum computing represents a paradigm shift in computational power, promising exponential speedups for solving certain classes of problems. However, harnessing the full potential of quantum computers requires effective utilization of data science techniques. In this review paper, we explore the intersection of data science and quantum computing, focusing on the role of data analytics in advancing quantum computing applications. We begin with an overview of quantum computing fundamentals, including quantum mechanics principles and quantum algorithms. We then delve into topics such as quantum data representation, manipulation, and machine learning algorithms tailored for quantum computing environments. Additionally, we discuss quantum error correction and noise mitigation strategies essential for reliable quantum computation. Furthermore, we survey the landscape of quantum software development tools and frameworks, highlighting their importance in facilitating quantum algorithm design and optimization. Through case studies and examples, we demonstrate the practical applications of data science techniques in quantum computing, including quantum cryptography and quantum-enhanced data analysis. Finally, we identify future research directions and challenges in the field, emphasizing the need for interdisciplinary collaboration between the data science and quantum computing communities to unlock the full potential of quantum data science.
Quantum computing, a revolutionary paradigm shift in computation, leverages the principles of quantum mechanics to perform tasks beyond the capabilities of classical computers. By harnessing the power of quantum bits (qubits), which can exist in superpositions of states and exhibit entanglement, quantum computers offer the potential to revolutionize fields such as chemistry, materials science, cryptography, and artificial intelligence. This paper delves into the fundamental concepts of quantum computing, including superposition, entanglement, and quantum gates. We explore the development of quantum hardware, encompassing various technologies like superconducting qubits, ion traps, and photonic qubits. Additionally, we discuss the limitations and challenges faced in building scalable and error-corrected quantum computers, such as decoherence and noise. The potential applications of quantum computing are vast and far-reaching. In the realm of quantum chemistry and materials science, quantum computers can simulate complex molecular systems, enabling the design of novel materials and drugs. In the field of cryptography, quantum computers pose a significant threat to classical cryptographic algorithms, necessitating the development of quantum-resistant cryptographic techniques. Furthermore, quantum machine learning holds the promise of accelerating AI advancements by enabling the development of powerful quantum algorithms.
Quantum computing is emerging as a powerful computational paradigm capable of solving infeasible problems for classical computers. This review explores the intersection of quantum computing and artificial intelligence (AI), focusing on how quantum algorithms can enhance AI applications. The purpose of this review is to provide a comprehensive analysis of current advancements in quantum machine learning (QML), quantum optimization, and quantum neural networks, highlighting their implications for data processing, cryptography, and decision-making systems. The scope of the literature includes peer-reviewed articles, conference papers, and technical reports from 2020 to 2025, focusing on quantum algorithms, applications in AI, and hardware limitations. Key findings indicate that quantum computing can significantly accelerate data processing tasks, optimize complex problem-solving techniques, and improve the efficiency of AI models. However, challenges such as quantum noise, error correction, and hardware scalability remain substantial barriers to widespread adoption. This review concludes with discussing future research directions, including hybrid quantum-classical systems and advanced quantum error correction methods. This study serves as a foundational reference for researchers and practitioners aiming to leverage quantum computing for AI-driven applications. 3. Keywords Quantum Computing, Artificial Intelligence, Quantum Machine Learning, Quantum Algorithms, Data Processing, Optimization, Quantum Neural Networks
Quantum computing represents a paradigm shift in computational capability that promises to transform data science by addressing problems currently intractable on classical systems. As global data creation expands exponentially, quantum technologies emerge as potential solutions for managing unprecedented complexity. This article examines the fundamental advantages of quantum computing, including exponential parallelism through superposition, management of computational complexity, and the ability to solve previously unsolvable problems. High-impact application domains are explored, including machine learning transformation, optimization at unprecedented scales, cryptography and data security, molecular simulation for drug discovery, and complex systems modeling for climate prediction. While these opportunities appear transformative, substantial technical challenges remain, including quantum decoherence, hardware scalability constraints, and algorithmic development gaps. The path forward likely involves hybrid quantum-classical architectures, targeted applications in big data analytics, enhanced AI systems, and revolutionary simulation capabilities for complex systems.
Quantum computing leverages quantum mechanical concepts, including superposition, entanglement, and interference, to solve problems that traditional computers cannot handle efficiently. This study presents a comprehensive overview of the field, covering both the theoretical foundations and the latest developments in quantum hardware and algorithms. This study covers fundamental elements, qubits, gates, and circuits while emphasizing quantum parallelism as a distinctive feature. To illustrate efficiency gains, we explore notable algorithms like Shor’s factorization procedure and Grover’s search algorithm. Furthermore, we analyze recent advancements in quantum hardware, focusing on superconducting qubits, trapped ions, and photonic quantum computers, with an emphasis on implementations by IBM, Google, and other industry leaders. The discussion also addresses major challenges such as decoherence, noise, and the need for quantum error correction to achieve fault tolerance. Finally, we explore emerging applications in cryptography, optimization, quantum machine learning, and material simulation, and identify critical open problems that must be addressed to realize scalable, universal quantum computing. Our review underscores the transformative potential of quantum technologies across science and industry.