Riassunto
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
Informazioni sull?autore
Constantin Gonciulea leads the Advanced Technology group at Wells Fargo. He holds advanced degrees in mathematics and computer science. Over the last 25 years, he has delivered major server-side, web, and mobile online banking platforms and products, and has worked in quantum computing since 2018.
Charlee Stefanski is a senior software engineer in the Advanced Technology group at Wells Fargo, where she leads the development of the internal quantum computing platform. She holds a BS from the University of Michigan and a Masters from UC Berkeley.
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