Manning Publications Co., 2025. — 376 p. — ISBN: 978-1633437630.
A developer-centric look at quantum computing.
Quantum computing opens a new realm of possibilities for developers in complex research and business domains like industrial simulation, predictive modeling, drug discovery, and operations research. Building Quantum Software with Python gives you the foundation you’ll need for the software for the quantum age.
Quantum computers are rapidly becoming a realistic alternative for complex research and business problems. Building Quantum Software with Python lays out the math and programming techniques you’ll need to apply quantum solutions to real challenges like predictions based on massive data sets and intricate simulations. You will learn which quantum algorithms and patterns apply to different types of problems and how to build your first quantum applications.
Building Quantum Software with Python builds your understanding of quantum computing by relating it to the classical computing concepts you already know. With careful explanations and thoughtful illustrations, it takes you from zero knowledge to creating quantum solutions that run on a simulator or real quantum hardware. The book utilizes intuitive visualizations of quantum systems with tables and diagrams developers will find instantly familiar, and code implementations that are easier to understand than complex algebra.
As you go, you’ll explore potential applications of quantum computing, including useful probability distributions for truly random sampling, quantum optimization solutions using the knapsack problem, and quantum solutions for unstructured search. All the simulator code you write can be easily converted to run on IBM Quantum hardware.
Foreword by Heather Higgins.
Purchase of the print book includes a free eBook in PDF and EPUB formats from Manning Publications.
About the technology.Large-scale optimization problems, complex financial and scientific simulations, cryptographic calculations, and certain types of machine learning require unreasonably long times to run on classical computers. Quantum computers can perform some operations like these almost instantaneously! Don’t wait to get started. This book will prime you on quantum applications, implementations, and hybrid quantum-classic designs so you’ll be ready to join the quantum revolution.
About the book.Building Quantum Software with Python teaches you how to build working applications that run on a simulator or real quantum hardware. By relating QC to classical computing concepts you already know, this book’s intuitive visualizations and code implementations make quantum computing easy to grasp even if you don’t have a background in advanced math. As you go, you’ll discover and implement quantum techniques for truly random sampling, optimization solutions, unstructured search, and more — all using easy-to-follow Python code.
What's inside?Hype-free discussions of when, where, and why QC makes sense.
Solving complex optimization problems.
Quantum search using Grover’s Algorithm.
Fourier transform, phase estimation, and probability distribution sampling.
For developers who know Python. No advanced math knowledge required.
Constantin Gonciulea leads the Advanced Technology group at Wells Fargo and has worked in quantum computing since 2018. Charlee Stefanski is a senior software engineer at Wells Fargo, where she leads the development of the internal quantum computing platform.
Part 1.
Advantages and challenges of programming quantum computers.
A first look at quantum computations: The knapsack problem.
Single-qubit states and gates.
Quantum state and circuits: Beyond one qubit.
Part 2.
Selecting outcomes with quantum oracles.
Quantum search and probability estimation.
The quantum Fourier transform.
Using the quantum Fourier transform.
Quantum phase estimation.
Part 3.
Encoding functions in quantum states.
Search-based quantum optimization.
Conclusions and outlook.
Appendixes.
A Math refresher.
B More about quantum states and gates.
C Outcome pairing strategies.