In our current world, numerous innovative approaches aimed at expansion and recommendation of queries are developing to satisfy the mounting tendencies of social networking and the internet. Many effectual procedures have been developed to achieve query expansion. Additionally, the creation of quantum-based procedures is now becoming an important task. Quantum based algorithms are the way to secure the future of computation by preparing for the advent of quantum computers. QuantQueryEXP implements an eminent technique, the Deutsch-Jozsa [1] algorithm, in an altered manner, delivering a substitute for expansion and recommendation of queries implementing quantum techniques which offers much higher accuracy with optimization in complexity over any conventional algorithm to achieve supremacy in quantum computing. QuantQueryEXP attains an accuracy of 90.43% along with great optimization of resource and temporal complexity.
In our current world, numerous innovative approaches aimed at expansion and recommendation of queries are developing to satisfy the mounting tendencies of social networking and the internet. Many effectual procedures have been developed to achieve query expansion. Additionally, the creation of quantum-based procedures is now becoming an important task. Quantum based algorithms are the way to secure the future of computation by preparing for the advent of quantum computers. QuantQueryEXP implements an eminent technique, the Deutsch-Jozsa [1] algorithm, in an altered manner, delivering a substitute for expansion and recommendation of queries implementing quantum techniques which offers much higher accuracy with optimization in complexity over any conventional algorithm to achieve supremacy in quantum computing. QuantQueryEXP attains an accuracy of 90.43% along with great optimization of resource and temporal complexity.
Abstract In 1985, David Deutsch proposed the original quantum algorithm, a simple yet remarkable demonstration of the enhanced computational power offered by quantum mechanics. Deutsch’s algorithm provides a reduction in the number of queries to an unknown function required to discern some property of that function, relative to the best possible classical counterpart. This is known as an advantage in query complexity, and whilst the attention of the quantum computing community has, by and large, moved on to quantum algorithms that provide an advantage in computational rather than query complexity, it is still instructive to study some of the most important quantum algorithms for query problems. Such algorithms are historically important and, moreover, reveal the source of the computational power of quantum mechanics in a clear and easy to understand manner. This chapter studies four algorithms for query problems: Deutsch’s algorithm, the Deutsch–Jozsa algorithm, the Bernstein–Vazirani algorithm, and Simon’s algorithm.
Quantum computing represents a transformative approach to computation that exploits the principles of quantum mechanics to process information in fundamentally different ways from classical computers. Over the past few decades, the field has evolved from a primarily theoretical discipline into an active area of research spanning computer science, mathematics, physics, information theory, and engineering. As quantum technologies continue to develop, understanding the fundamental computational principles underlying quantum algorithms has become increasingly important. Foundations of Quantum Computing: The Query Model Perspective presents a systematic introduction to quantum computing with particular emphasis on the query model, a powerful framework for studying the computational complexity of quantum algorithms. The query model provides a clean and mathematically tractable setting in which the number of queries made to an unknown function or oracle can be used as the primary measure of computational cost. This perspective offers valuable insights into the capabilities and limitations of quantum computation and provides a natural foundation for understanding quantum speedups. The book introduces the fundamental concepts required to study quantum computation, including quantum states, quantum gates, quantum circuits, measurement, superposition, entanglement, and quantum algorithms. These concepts are developed alongside the query-complexity framework, enabling readers to understand how quantum algorithms exploit interference and other quantum phenomena to reduce the number of queries required to solve computational problems. Particular attention is given to important quantum query algorithms and complexity results. Classical and quantum query complexities are discussed comparatively to illustrate the sources and significance of quantum advantages. Well-known examples such as the Deutsch–Jozsa algorithm, Grover's search algorithm, and related oracle-based problems provide concrete illustrations of how quantum procedures can outperform their classical counterparts. The discussion also introduces lower-bound techniques and fundamental principles that help establish the limitations of quantum query algorithms. The query model is not only useful for understanding individual algorithms but also serves as an important bridge between quantum algorithms and computational complexity theory. By focusing on query complexity, many problems can be examined independently of the details of a particular physical implementation. This abstraction makes the framework especially useful for developing rigorous proofs, comparing computational resources, and identifying the fundamental sources of quantum advantage. This book is intended for students, researchers, educators, and professionals interested in quantum computing and theoretical computer science. It can serve as an introductory reference for readers with a background in discrete mathematics, linear algebra, algorithms, or theoretical computer science, while also providing a foundation for readers who wish to pursue more advanced studies in quantum complexity theory and quantum algorithms. The chapters have been organized to progress from foundational concepts toward more advanced aspects of the quantum query model. Mathematical formulations are accompanied by explanations and examples to help readers connect abstract theoretical ideas with computational intuition. The broader objective is to provide readers with the tools necessary not only to understand existing quantum algorithms but also to reason about new problems from a query-complexity perspective. As quantum computing continues to mature, the ability to distinguish genuine computational advantages from implementation-specific effects will become increasingly significant. The query model provides one of the clearest theoretical environments for making this distinction. By studying quantum computation through this lens, readers can develop a deeper appreciation of both the power and the limitations of quantum algorithms It is hoped that Foundations of Quantum Computing: The Query Model Perspective will serve as a useful resource for learning, teaching, and research, and will encourage further exploration into the rapidly evolving landscape of quantum computation and quantum complexity theory.
Cloud database platforms face considerable processing limitations when managing complex relational queries that involve intricate joins and layered optimization challenges. Quantum computing techniques offer groundbreaking solutions by integrating quantum principles into conventional hardware systems.These advanced methodologies implement tensor network architectures, simulated annealing processes, and quantum-influenced sampling procedures to examine multiple query execution routes concurrently, substantially minimizing processing demands for elaborate analytical operations. Assessment findings indicate remarkable performance enhancements, with quantum-inspired optimization producing significant acceleration improvements for specialized analytical processes involving extensive graph relationships and combinatorial data configurations. These enhancements show particular effectiveness for complex queries that typically pose challenges to traditional database optimization methods. Deployment strategies focus on smooth integration with current SQL optimization systems while maintaining compatibility with existing relational database infrastructures. Technical implementation requires advanced hardware acceleration capabilities and strategic workload identification procedures that allow organizations to optimize performance advantages. Integration obstacles include maintaining compatibility with current optimization frameworks, optimizing resource distribution, and systematically evaluating query patterns appropriate for quantum-inspired processing methods. Database architects and performance engineers gain valuable insights into how these quantum-inspired approaches constitute substantial evolutionary progress beyond conventional parallel processing methods. These techniques create fundamental principles for advanced database optimization while maintaining operational compatibility with current relational database management systems, delivering scalable solutions for complex analytical processing demands across diverse cloud computing environments.
This paper presents a comprehensive review and systematic coverage of foundational quantum optimization algorithms-QAOA, Quantum Annealing, VQE, VQLS, and HHL-tracing their development all the way from Schrödinger's equation through gate-based quantum computing (Deutsch, Deutsch-Jozsa, Bernstein-Vazirani, QFT, Shor's, and Grover's algorithms) to hybrid quantum-classical approaches for NISQ-era hardware. We also present the transverse field Ising model's role in quantum annealing as well as practical hardware implementations with the Chimera, Pegasus, and Zephyr topologies of the D-Wave quantum annealer, addressing key challenges including barren plateaus, circuit trainability, and noise resilience. In the simulations section, quantum computing applications to collaborative filtering and social network optimization have been presented. We introduce two original contributions: (1) a quantum collaborative filtering model based on VQE principles that leverages quantum entanglement to represent user-item relationships, requiring logarithmically fewer parameters than classical embedding methods while accessing exponentially larger state spaces, and demonstrating test RMSE of 0.85 on synthetic data; and (2) a Quantum Social Network (QSN) framework modeling belief dynamics through superposition and entanglement, revealing emergent consensus and polarization phenomena validated via Qiskit simulations. By demonstrating that variational quantum algorithms can tackle real-world recommendation and network optimization problems on current hardware, we bridge theoretical quantum computing with practical machine learning applications and identify concrete pathways toward quantum advantage beyond traditional combinatorial optimization.
This book, Quantum Computing: Algorithms, Architectures and Applications, has been carefully developed as a comprehensive resource to navigate the dynamic and complex world of quantum technologies. Intended for students, researchers, engineers, and technology enthusiasts, it aims to connect fundamental concepts with real-world implementation. The journey begins with the core principles of quantum mechanics relevant to computing, laying the groundwork that differentiates quantum systems from classical approaches. It further explores quantum gates, circuits, and computational logic, along with key topics such as error correction, system architectures, entanglement, superposition, and the no-cloning theorem. As the discussion progresses, the book examines foundational quantum algorithms, including the Deutsch–Jozsa algorithm, Simon’s problem, and others. These sections not only explain technical concepts but also encourage readers to rethink computational possibilities and limitations. The book also highlights practical applications and industry adoption. Quantum computing is increasingly influencing fields such as cryptography, finance, drug discovery, and climate science. Through global and regional case studies, readers gain insight into how various sectors are embracing this technological shift. A clear and structured approach has been maintained throughout, making the content accessible to beginners while still valuable for experienced professionals. The aim is to balance depth with clarity, ensuring both rigor and ease of understanding. While quantum computing continues to evolve with ongoing advancements and discoveries, the driving forces behind its progress remain curiosity, innovation, and collaboration.
A groundbreaking advancement in computing theory, quantum search algorithms leverage quantum mechanics to outperform their classical counterparts in solving search issues. With a focus on their theoretical underpinnings, practical applications, and performance in a variety of computational contexts, this systematic review carefully investigates the evolution, progress, and current state of quantum search algorithms. We present a thorough analysis of significant algorithms, such as Grover’s search algorithm, together with recent developments in quantum optimization and quantum-enhanced search techniques. We also go over the challenges with quantum error correction, scalability, and hardware constraints that prevent the broad application of quantum search techniques.