Quantum Computing (QC) is an emerging research field that is attracting significant interest from the scientific community. In fact, it is believed that quantum computers can be employed to solve complex computational problems more efficiently than traditional computers, due to their inherent capabilities of exploring large search spaces very efficiently by leveraging the principles of quantum mechanics, such as superposition, entanglement, and tunnelling. However, quantum computers are still in their early stages of development and their applications are very limited, especially in the field of Information Access (IA). Nevertheless, IA systems often face complex optimization problems that might be solved more efficiently through the usage of quantum computers. This paper outlines the author's PhD objectives in designing new methodologies for the application and evaluation of QC technologies for IA problems. Furthermore, this work provides an overview of the achieved preliminary results and a discussion of possible future research directions.
Quantum Computing (QC) is an emerging research field that is attracting significant interest from the scientific community. In fact, it is believed that quantum computers can be employed to solve complex computational problems more efficiently than traditional computers, due to their inherent capabilities of exploring large search spaces very efficiently by leveraging the principles of quantum mechanics, such as superposition, entanglement, and tunnelling. However, quantum computers are still in their early stages of development and their applications are very limited, especially in the field of Information Access (IA). Nevertheless, IA systems often face complex optimization problems that might be solved more efficiently through the usage of quantum computers. This paper outlines the author's PhD objectives in designing new methodologies for the application and evaluation of QC technologies for IA problems. Furthermore, this work provides an overview of the achieved preliminary results and a discussion of possible future research directions.
Quantum computing represents a monumental leap in computational technology, leveraging the principles of quantum mechanics to solve complex problems at unprecedented speeds. Unlike classical computers, which rely on bits as the smallest unit of information, quantum computers utilize qubits that can exist in multiple states simultaneously, enabling them to perform many calculations at once. This paper delves into the foundational concepts of quantum computing, including superposition, entanglement, and quantum gates, and explores its potential applications across various domains, such as cryptography, drug discovery, optimization problems, and artificial intelligence. We will discuss current advancements in quantum technology, high-lighting key breakthroughs and the progress made by leading research institutions and companies. Furthermore, this paper addresses the significant challenges that quantum computing faces, including error rates, qubit coherence, and scalability, which hinder its widespread adoption. By examining these facets, we aim to provide a comprehensive overview of how quantum computing could transform various fields and the hurdles that must be overcome to realize its full potential, ultimately suggesting future directions for research and development in this revolutionary area of technology.
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.
Quantum computing represents a transformative shift from classical computational paradigms by utilizing quantum mechanical principles such as superposition, entanglement, and quantum interference.In the era of Artificial Intelligence (AI), the integration of quantum computing with information technology opens new possibilities for solving complex computational problems beyond the capabilities of traditional systems.This paper explores the conceptual foundations of quantum computing and its convergence with AI-driven information technologies.It examines how quantum algorithms enhance machine learning efficiency, optimize large-scale data processing, and strengthen cybersecurity through quantum cryptography.Furthermore, the study discusses current technological challenges including hardware instability, error correction, scalability issues, and the need for interdisciplinary expertise.Using analytical and secondary data sources, the paper evaluates emerging applications in healthcare, finance, communication networks, and scientific simulations.The research concludes that quantum-AI integration will redefine computational intelligence, enabling faster decision-making and innovative technological solutions.The study highlights future research directions and emphasizes the importance of national-level academic collaboration to accelerate quantum technology adoption.
Quantum computing (QC) presents transformative potential for solving complex scientific problems that remain intractable for classical computers. Leveraging quantum superposition, entanglement, and interference, quantum algorithms such as Shor’s factoring algorithm, Grover’s search algorithm, variational quantum eigensolvers (VQE), and quantum approximate optimization algorithms (QAOA) enable exponential or polynomial speedups for selected problem classes. This paper explores recent advancements in quantum computing algorithms, their practical implementations, and emerging applications in fields such as molecular simulations, material science, cryptography, optimization, and machine learning. We highlight the strengths and limitations of current approaches, present performance analyses through graphical comparisons, and discuss prospective directions for integrating quantum algorithms into real-world scientific workflows. Our findings demonstrate that quantum computing is gradually transitioning from theoretical frameworks to experimental platforms capable of addressing highly complex computational challenges.
Quantum computing (QC) presents transformative potential for solving complex scientific problems that remain intractable for classical computers. Leveraging quantum superposition, entanglement, and interference, quantum algorithms such as Shor’s factoring algorithm, Grover’s search algorithm, variational quantum eigensolvers (VQE), and quantum approximate optimization algorithms (QAOA) enable exponential or polynomial speedups for selected problem classes. This paper explores recent advancements in quantum computing algorithms, their practical implementations, and emerging applications in fields such as molecular simulations, material science, cryptography, optimization, and machine learning. We highlight the strengths and limitations of current approaches, present performance analyses through graphical comparisons, and discuss prospective directions for integrating quantum algorithms into real-world scientific workflows. Our findings demonstrate that quantum computing is gradually transitioning from theoretical frameworks to experimental platforms capable of addressing highly complex computational challenges.
ABSTRACT Quantum computing represents a fundamental shift in the way computational problems are approached and solved. Unlike classical computing systems that rely on binary bits to process information, quantum computing utilizes quantum bits, or qubits, which exploit principles of quantum mechanics such as superposition and entanglement. This transition from classical bits to qubits has opened new possibilities for solving complex problems that are computationally infeasible for traditional systems. This paper explores the evolution of computing paradigms from classical architectures to quantum-based models, highlighting the theoretical foundations and technological advancements that have driven this transformation. It discusses the core concepts of quantum computing, including qubit representation, quantum gates, and computational models, along with an overview of current quantum hardware architectures. Furthermore, the paper examines key application areas such as cryptography, optimization, artificial intelligence, and scientific simulations, where quantum computing demonstrates significant potential. Despite its promise, quantum computing faces substantial challenges related to hardware stability, error correction, scalability, and resource requirements, which are also analyzed. Finally, the paper outlines future research directions and the potential impact of quantum computing on next-generation information processing systems, emphasizing its role in redefining the boundaries of modern computation. Keywords : Quantum Computing, Qubits, Quantum Algorithms, Quantum Applications, Future Computing Technologies