Description
This book integrates the foundations of quantum computing with a hands-on coding approach to this emerging field; it is the first to bring these elements together in an updated manner. This work is suitable for both academic coursework and corporate technical training.
The second edition includes extensive updates and revisions, both to textual content and to the code. Sections have been added on quantum machine learning, quantum error correction, Dirac notation and more. This new edition benefits from the input of the many faculty, students, corporate engineering teams, and independent readers who have used the first edition.
This volume comprises three books under one cover: Part I outlines the necessary foundations of quantum computing and quantum circuits. Part II walks through the canon of quantum computing algorithms and provides code on a range of quantum computing methods in current use. Part III covers the mathematical toolkit required to master quantum computing. Additional resources include a table of operators and circuit elements and a companion GitHub site providing code and updates.
Jack D. Hidary is a research scientist in quantum computing and in AI at Alphabet X, formerly Google X.
"Quantum Computing will change our world in unexpected ways. Everything technology leaders, engineers and graduate students need is in this book including the methods and hands-on code to program on this novel platform."
--Eric Schmidt, PhD, Former Chairman and CEO of Google; Founder, Innovation Endeavors
Author: Jack D. Hidary
Publisher: Springer
Published: 09/19/2021
Pages: 422
Binding Type: Hardcover
Weight: 2.00lbs
Size: 9.40h x 6.30w x 0.90d
ISBN13: 9783030832735
ISBN10: 3030832732
BISAC Categories:
- Science | Physics | Quantum Theory
- Computers | General
About the Author
Jack D. Hidary focuses on AI and on quantum computing at Alphabet X, formerly Google X. He and his group develop and research algorithms for NISQ-regime quantum processors as well as create new software libraries for quantum computing. In the AI field, Jack and his group focus on fundamental research such as the generalization of deep networks as well as applied AI technologies.