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. Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThis 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 2.0. 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 forThis 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.

About the Author
Elias 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, Elias 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 Gonzalez-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).


Book Information
ISBN 9781835885949
Author Elias F. Combarro
Format Paperback
Imprint Packt Publishing Limited
Publisher Packt Publishing Limited