Overview
- Teaches you how to design and develop your own quantum solutions
- Shows you how to implement popular quantum algorithms in an appropriate context in real life
- Demonstrates how quantum algorithms speed up machine learning and algorithms compared to classical ones
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Table of contents (14 chapters)
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Front Matter
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Introduction
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Front Matter
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Quantum Computing Basics
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Front Matter
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Back Matter
Keywords
About this book
Know how to use quantum computing solutions involving artificial intelligence (AI) algorithms and applications across different disciplines.
Quantum solutions involve building quantum algorithms that improve computational tasks within quantum computing, AI, data science, and machine learning. As opposed to quantum computer innovation, quantum solutions offer automation, cost reduction, and other efficiencies to the problems they tackle.
Starting with the basics, this book covers subsystems and properties as well as the information processing network before covering quantum simulators. Solutions such as the Traveling Salesman Problem, quantum cryptography, scheduling, and cybersecurity are discussed in step-by-step detail.
The book presents code samples based on real-life problems in a variety of industries, such as risk assessment and fraud detection in banking. In pharma, you will look at drug discovery and protein-folding solutions. Supply chain optimizationand purchasing solutions are presented in the manufacturing domain. In the area of utilities, energy distribution and optimization problems and solutions are explained. Advertising scheduling and revenue optimization solutions are included from media and technology verticals.What You Will Learn
- Understand the mathematics behind quantum computing
- Know the solution benefits, such as automation, cost reduction, and efficiencies
- Be familiar with the quantum subsystems and properties, including states, protocols, operations, and transformations
- Be aware of the quantum classification algorithms: classifiers, and support and sparse support vector machines
- Use AI algorithms, including probability, walks, search, deep learning, and parallelism
Who This Book Is For
Developers in Python and other languages interested inquantum solutions. The secondary audience includes IT professionals and academia in mathematics and physics. A tertiary audience is those in industry verticals such as manufacturing, banking, and pharma.
Authors and Affiliations
About the author
Bhagvan founded Quantica Computacao, the first quantum computing startup in India. He has engineered and developed simulators and tools in quantum technology using IBM Q, Microsoft Q#, and Google QScript. The company's focus is developing quantum cryptographic tools to provide quantum proof data security, which will help banking institutions protect their transactions. He is now Director of Product Engineering at Value Momentum. Value Momentum has a social networkfor doctors (White Coats) and provides telehealth support through the Practice Plus suite of products and services.
Bhagvan has published papers and presented at IEEE, AstriCon, Avios, DevCon, PyCon, and genomics and biotechnology conferences on topics including adaptive learning, AI Coder, and more. He is a hands-on CTO who has been contributing to open source, blogs, and the latest technologies such as Go, Python, Django, node.js and Java, MySQL, Postgres, Mongo, and Cassandra. He is the technical reviewer for the book Machine Learning with TensorFlow. He has written the book Hands-On Data Structures and Algorithms with Go and Paytech and The Payment Technology Handbook for Investors, Entrepreneurs, and FinTech Visionaries.
Bibliographic Information
Book Title: Quantum Computing Solutions
Book Subtitle: Solving Real-World Problems Using Quantum Computing and Algorithms
Authors: Bhagvan Kommadi
DOI: https://doi.org/10.1007/978-1-4842-6516-1
Publisher: Apress Berkeley, CA
eBook Packages: Business and Management, Apress Access Books, Business and Management (R0)
Copyright Information: Bhagvan Kommadi 2020
Softcover ISBN: 978-1-4842-6515-4Published: 21 December 2020
eBook ISBN: 978-1-4842-6516-1Published: 20 December 2020
Edition Number: 1
Number of Pages: XVII, 300
Number of Illustrations: 131 b/w illustrations
Topics: Big Data, Python, Quantum Computing