Authors: Dr. B. Venkateswaralu, Dr. M. V. N. Srujan Manohar, Dr. Poornima Ramaraju, and Dr. A. Mathiarasu

ISBN: 978-81-69857-95-6

DOI: https://doi.org/10.59646/854

Date of Publication: October 20, 2026

Preface

Quantum Computing for Computer Scientists provides a comprehensive introduction to the principles, mathematical foundations, computational models, algorithms, and programming techniques that underpin quantum computing. As an emerging paradigm that extends beyond the capabilities of conventional information processing, quantum computing offers new approaches to solving computationally intensive problems through superposition, entanglement, interference, and quantum measurement. This book bridges the gap between classical computer science and quantum information processing by presenting essential concepts in a structured and technically oriented manner. It begins with qubits, quantum states, linear algebra, Dirac notation, quantum gates, and circuit representations, before progressing to quantum computational models, algorithmic speedups, complexity theory, error correction, and quantum software development. Particular emphasis is placed on understanding how quantum principles influence algorithm design, computational efficiency, and the theoretical limits of information processing.

The book is organized into six units, each addressing a fundamental dimension of quantum computing. The first unit establishes the mathematical and conceptual foundations required to understand quantum states, unitary transformations, quantum circuits, and probabilistic measurement outcomes. The second unit explores quantum logic, universal gate sets, circuit optimization, reversible computing, quantum Turing machines, measurement-based computation, and adiabatic models. The third unit examines prominent quantum algorithms, including the Deutsch–Jozsa algorithm, Grover’s search algorithm, Shor’s factoring algorithm, quantum Fourier transform, phase estimation, and quantum walks, while critically examining the limitations of quantum speedup. The fourth unit introduces quantum complexity theory through classes such as BQP, QMA, and QCMA, alongside quantum query complexity, communication complexity, lower bounds, and the relationships between classical and quantum computational models. The fifth unit focuses on quantum noise, decoherence, quantum error correction, syndrome measurement, fault-tolerant operations, surface codes, and the challenges of building scalable quantum systems. The final unit introduces quantum programming languages and software frameworks, including Qiskit, Cirq, Q#, and PyQuil, together with quantum compilers, hybrid quantum–classical workflows, simulation, cloud-based platforms, benchmarking, debugging, and verification. Through this progression, the book aims to equip students, researchers, and computing professionals with the theoretical understanding and practical knowledge necessary to explore quantum algorithms, evaluate quantum computational systems, and develop software for emerging quantum platforms. By connecting mathematical principles with algorithmic reasoning and implementation techniques, it serves as a valuable academic resource for understanding the foundations and evolving capabilities of quantum computing from a computer science perspective.