Quantum Computing: Principles Progr...

Quantum Computing: Principles Progr...

Publisher
Independently published
Published
2025
Pages
—
ISBN-13
9798319216465
Format
print
Language
English
Book Summary
Quantum Computing: Principles, Programming, and Applications by Anshuman Mishra is a comprehensive textbook that introduces the core concepts of quantum computing, from qubits and quantum gates to algorithms and real-world applications. It balances theoretical foundations with practical programming exercises, making it suitable for students and professionals seeking a solid, hands-on understanding of this rapidly evolving field.
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About Quantum Computing: Principles Progr...

This textbook provides a rigorous yet accessible introduction to quantum computing, covering the mathematical and physical principles underlying quantum information processing. Readers will explore qubits, superposition, entanglement, quantum gates, and circuit models, before moving on to key algorithms such as Shor's and Grover's. The book emphasizes a hands-on approach, integrating programming examples and exercises using popular quantum frameworks like Qiskit and Cirq. Later chapters discuss applications in cryptography, optimization, and machine learning, as well as the challenges of building scalable quantum hardware. With its clear explanations, worked examples, and end-of-chapter problems, this book equips readers with both the theoretical knowledge and practical skills needed to engage with quantum computing. It is designed for a one-semester course or self-study, assuming only basic familiarity with linear algebra and programming.

Who should read Quantum Computing: Principles Progr...?

This book is ideal for undergraduate and graduate students in computer science, physics, and engineering who want a structured introduction to quantum computing. It also suits software developers and IT professionals looking to transition into quantum programming, as well as researchers in related fields seeking a foundational reference. Instructors will find it useful as a course text. A basic understanding of linear algebra and Python programming is recommended, but no prior quantum mechanics knowledge is required.

Pros & Cons
Pros
  • ✓ Comprehensive coverage from basic principles to advanced algorithms
  • ✓ Hands-on programming examples using Qiskit and Cirq
  • ✓ Clear explanations with worked examples and end-of-chapter exercises
  • ✓ Balances theory with practical applications in cryptography and optimization
  • ✓ Suitable for both classroom use and self-study
Cons
  • ✗ Assumes some prior knowledge of linear algebra and programming
  • ✗ May be too introductory for advanced researchers seeking cutting-edge topics
  • ✗ Limited coverage of quantum error correction and hardware details
  • ✗ Some readers may find the mathematical formalism challenging without supplementary material
  • ✗ No companion website or downloadable code repository mentioned
Frequently asked questions
Do I need prior knowledge of quantum mechanics to read this book?
No, the book introduces quantum mechanics concepts from the ground up, focusing on the necessary principles for quantum computing. However, basic familiarity with linear algebra and programming is helpful.
Which programming languages and frameworks are used in the book?
The book primarily uses Python with popular quantum computing frameworks such as Qiskit and Cirq. Examples and exercises are provided to help readers practice coding quantum circuits.
Is this book suitable for self-study?
Yes, the book is designed to be accessible for self-learners, with clear explanations, worked examples, and exercises. It can also serve as a textbook for a one-semester course.
Does the book cover advanced topics like quantum error correction?
While the book touches on error correction, it is not the main focus. It provides a solid foundation in core principles and algorithms, with some discussion of hardware challenges.
Are there solutions to the exercises available?
The book includes exercises but does not mention a solutions manual. Instructors may need to develop their own solutions or use it as a basis for classroom discussion.

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