Foundations of Quantum Computing: Concepts, Algorithms, and Real-World Challenges

Hanwen Liu, Yiran Li, Ibrahim Sabek +2 more

Quantum computing has recently moved from a theoretical concept to a transformative and increasingly accessible computational paradigm. These developments sparked growing interest in the database community along two complementary directions: applying quantum techniques to improve database tasks, and leveraging DBMS principles to support quantum workloads. This three-hour tutorial provides a bidirectional, practice-oriented treatment of this intersection, covering quantum foundations, optimization algorithms, a systematic survey of Quantum-for-DB and DB-for-Quantum research, and end-to-end system integration with a real DBMS. The tutorial includes hands-on demonstrations using quantum simulators and real quantum hardware. We summarize open challenges and research opportunities for practical real-world deployment.

Venkateswaran Kasirajan

K.V. Rama Raoa, N. Srinivasb, K. Rama Krishnaiahc +1 more

Quantum computing leverages quantum-mechanical phenomena such as superposition, entanglement and interference to solve classes of problems that are intractable on classical machines.This paper unifies three complementary perspectives: (i) the central role of complex matrices in modelling quantum states, gates and error-correction protocols; (ii) the pervasive use of trigonometric functions in qubit representation, phase manipulation and the quantum Fourier transform (QFT); and (iii) a systematic comparison of classical and quantum computing paradigms with illustrative circuits and real-world applications.By integrating these dimensions, the study provides a consolidated reference that elucidates the mathematical foundations, circuit constructions, algorithmic advantages and practical challenges that define contemporary quantum information processing.

Nirmala Priyadharshini P, Mr. Mohammed Ansar Ali M A, Mrs. Rajaprabha +1 more

Masahito Hayashi, Satoshi Ishizaka, Akinori Kawachi +2 more

Anchal Satpute, Madhavi Avhankar

Quantum Machine Learning (QML) has become one of the leading interdisciplinary applications in terms of computational sciences.It leverages quantum-computing principles for machine learning tasks, providing new computational concepts to address complex, high-dimensional problems.In this review article, we offer a comprehensive discussion of the theoretical foundation, algorithmic models, hardware limitations, and experimental realizations of QML.In other words, there is a very big difference between the speed improvements which quantum computing promises in the theory world and what we can actually achieve when running the algorithms in real world.The review consolidates the results from classical papers, reviews the hybrid architectures, and evaluates the challenges in reproducibility in the field.A Paper for a Scholarly Journal.The paper is scholarly and will be of use to scholars, researchers, teachers, and practitioners who attend national conferences.[8], [2]