Building Quantum Software In Python
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Building Quantum Software in Python
Author: Constantin Gonciulea
language: en
Publisher: Simon and Schuster
Release Date: 2025-05-27
A developer-centric look at quantum computing. The demand for developers who can implement solutions with quantum resources is growing larger every day. Building Quantum Software with Python gives you the foundation you need to build the software for the quantum age, and apply quantum computing to real-world business and research problems. In Building Quantum Software with Python you will learn about: • Quantum states, gates, and circuits • A practical introduction to quantum algorithms • Running quantum software on classical simulators and quantum hardware • Quantum search, phase estimation, and quantum counting • Quantum solutions to optimization problems Building Quantum Software with Python lays out the math and programming techniques you’ll need to apply quantum solutions to real challenges like sampling from classically intractable probability distributions and large-scale optimization problems. You will learn which quantum algorithms and patterns apply to different types of problems and how to build your first quantum applications. All the simulator code you write can be easily converted to run on real 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 About the reader For developers who know Python. No advanced math knowledge required. About the author 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. Table of Contents Part 1 1 Advantages and challenges of programming quantum computers 2 A first look at quantum computations: The knapsack problem 3 Single-qubit states and gates 4 Quantum state and circuits: Beyond one qubit Part 2 5 Selecting outcomes with quantum oracles 6 Quantum search and probability estimation 7 The quantum Fourier transform 8 Using the quantum Fourier transform 9 Quantum phase estimation Part 3 10 Encoding functions in quantum states 11 Search-based quantum optimization 12 Conclusions and outlook Appendixes A Math refresher B More about quantum states and gates C Outcome pairing strategies
Building Quantum Software with Python
Author: Constantin Gonciulea
language: en
Publisher: Simon and Schuster
Release Date: 2025-05-13
A developer-centric look at quantum computing. The demand for developers who can implement solutions with quantum resources is growing larger every day. Building Quantum Software with Python gives you the foundation you need to build the software for the quantum age, and apply quantum computing to real-world business and research problems. In Building Quantum Software with Python you will learn about: • Quantum states, gates, and circuits • A practical introduction to quantum algorithms • Running quantum software on classical simulators and quantum hardware • Quantum search, phase estimation, and quantum counting • Quantum solutions to optimization problems Building Quantum Software with Python lays out the math and programming techniques you’ll need to apply quantum solutions to real challenges like sampling from classically intractable probability distributions and large-scale optimization problems. You will learn which quantum algorithms and patterns apply to different types of problems and how to build your first quantum applications. All the simulator code you write can be easily converted to run on real quantum hardware. Foreword by Heather Higgins. 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 About the reader For developers who know Python. No advanced math knowledge required. About the author 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. Table of Contents Part 1 1 Advantages and challenges of programming quantum computers 2 A first look at quantum computations: The knapsack problem 3 Single-qubit states and gates 4 Quantum state and circuits: Beyond one qubit Part 2 5 Selecting outcomes with quantum oracles 6 Quantum search and probability estimation 7 The quantum Fourier transform 8 Using the quantum Fourier transform 9 Quantum phase estimation Part 3 10 Encoding functions in quantum states 11 Search-based quantum optimization 12 Conclusions and outlook Appendixes A Math refresher B More about quantum states and gates C Outcome pairing strategies
Quantum Software Engineering
This book presents a set of software engineering techniques and tools to improve the productivity and assure the quality in quantum software development. Through the collaboration of the software engineering community with the quantum computing community new architectural paradigms for quantum-enabled computing systems will be anticipated and developed. The book starts with a chapter that introduces the main concepts and general foundations related to quantum computing. This is followed by a number of chapters dealing with the quantum software engineering methods and techniques. Topics like the Talavera Manifesto for quantum software engineering, frameworks for hybrid systems, formal methods for quantum software engineering, quantum software modelling languages, and reengineering for quantum software are covered in this part. A second set of chapters then deals with quantum software environments and tools, detailing platforms like QuantumPath®, Classiq as well as quantum software frameworks for deep learning. Overall, the book aims at academic researchers and practitioners involved in the creation of quantum information systems and software platforms. It is assumed that readers have a background in traditional software engineering and information systems.