A Practical Guide to Quantum Computing

Learn about quantum information processing with Qiskit through hands-on projects. A foundational resource for STEM professionals, researchers and university students interested in quantum computers and algorithms.

Key Features

• Understand the theoretical foundations of quantum computing
• Learn how to use the Qiskit framework and how to run quantum algorithms with it
• Discover top quantum algorithms like Grover’s search and Shor’s factoring methods

Book Description

This book is an introduction, from scratch, to quantum computing and the most important and foundational quantum algorithms—ranging from humble protocols such as Deutsch’s algorithm to ones with far-reaching potential, such as Shor’s factoring algorithm—offering clear explanations and a hands-on approach with runnable code on simulators and real hardware. The book is self-contained and does not assume any previous experience in quantum computing. Starting with a single qubit, it scales to algorithms using superposition and entanglement.

At every step, examples of applications are provided, including how to create quantum money that is impossible to forge, quantum cryptography that cannot be broken, and algorithms for searching and factoring that are much faster than those that regular, non-quantum computers can use. Code for each of these algorithms is provided (and explained in detail) using Qiskit 2.1.

After reading this book, you will understand how quantum algorithms work, how to write your own quantum programs, and how to run them on quantum simulators and actual quantum computers. You will also be prepared to take the jump into quantum algorithms for optimization and artificial intelligence, like those presented in our previous book, A Practical Guide to Quantum Machine Learning and Quantum Optimization.

What you will learn

• Understand what makes a quantum computer unique
• Mathematically represent the state of multi-qubit systems
• Describe the effects of measurements in quantum computers
• Know how quantum superposition, entanglement, and interference work
• Implement and run any quantum algorithm in Qiskit
• Understand how Shor’s and Grover’s algorithms work
• Gain familiarity with quantum fault-tolerance and quantum advantage

Who this book is for

This book would be ideal for university-level students in Computer Science, Mathematics, Physics or other STEM fields taking introductory-level courses on quantum computing. It also suits professionals, researchers and self-learners with a STEM background. Potential readers of our previous book, A Practical Guide to Quantum Machine Learning and Quantum Optimization, will benefit from first building foundational quantum computing skills with this book.

Table of Contents

Part 1: One Qubit to Rule Them All: Working with One Qubit

Chapter 1: What Is (and What Is Not) a Quantum Computer

Chapter 2: Qubits, Gates, and Measurements

Chapter 3: Applications and Protocols with One Qubit

Chapter 4: Coding One-Qubit Protocols in Qiskit

Part 2: Qubit Meets Qubit: Two Qubits and Entanglement

Chapter 5: How to Work with Two Qubits

Chapter 6: Applications and Protocols with Two Qubits

Part 3: Working with Many Qubits

Chapter 7: Coding Two-Qubit Algorithms in Qiskit

Chapter 8: How to Work with Many Qubits

Chapter 9: The Full Power of Quantum Algorithms

Chapter 10: Coding with Many Qubits in Qiskit

Part 4: The Stars of the Show: Main Quantum Algorithms

Chapter 11: Finding the Period and Factoring Numbers

Chapter 12: Searching and Counting with a Quantum Computer

Chapter 13: Coding Shor and Grover’s Algorithms in Qiskit

Part 5: Ad Astra: The Road to Quantum Utility and Advantage

Chapter 14: Quantum Error Correction and Fault Tolerance

Chapter 15: Experiments for Quantum Advantage

Appendices

Appendix A: Mathematical Tools

Appendix B: The Bra-Ket Notation and Other Foundational Notions

Appendix C: Measuring the Complexity of Algorithms

Chapter D: Installing the Tools

Chapter E: Production Notes

Solutions

About the Authors

Elías F. Combarro holds degrees in both Mathematics (1997, second highest grades in Spain) and Computer Science (2002, highest grades in Spain). After some research visits at the Novosibirsk State University (Russia), he obtained a Ph.D. in Mathematics (2001). Since 2023, Elías F. Combarro has been a full professor at the Computer Science Department of the University of Oviedo (Spain). He has been a visiting scientist at CERN and Harvard University. Currently, he is Spain representative in the Advisory Board of CERN Quantum Technology Initiative, a member of the Advisory Board of SheQuantum and one of the founders of QSpain. He is one of the authors of A Practical Guide to Quantum Machine Learning and Quantum Optimization (Packt, 2023).

Samuel González-Castillo holds degrees from the University of Oviedo (Spain) in Mathematics and Physics (2021) and a Research Master’s Degree in Mathematics from Maynooth University (2023). He is a mathematics research student and graduate teaching assistant at the University of Oviedo, focusing on applying algebraic techniques to problems in quantum computing. In 2021, he developed a benchmarking framework for quantum simulators at CERN openlab. He has contributed to several conferences on quantum computing, including the Quantum Technology International Conference and the Conference on Computing in High Energy and Nuclear Physics. He is the co-author of A Practical Guide to Quantum Machine Learning and Quantum Optimization (Packt, 2023).