Quantum Computing: An Applied Approach [Hardcover] Hidary, Jack D.

Hidary, Jack D.

ISBN 10: 3030832732 / ISBN 13: 9783030832735

Published by Springer, 2021

About this Item

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Bibliographic Details

• Title: Quantum Computing: An Applied Approach [Hardcover] Hidary, Jack D.
• Publisher: Springer
• Publication Date: 2021
• Language: English
• ISBN 10: 3030832732
• ISBN 13: 9783030832735
• Binding: Hardcover
• Edition: 2nd Edition
• Condition: New
• Seller catalogs:
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  • VCF

About this title

Synopsis

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.

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.