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.
Table of Contents:
Part 1: Introduction.- Chapter 1: Quantum Solutions Overview.- Chapter 2: Mathematics Behind Quantum Computing.- Part 2: Quantum Computing Basics.- Chapter 3: Quantum SubSystems and Properties.- Chapter 4: Quantum Information Processing Framework.- Chapter 5: Quantum Simulators.- Chapter 6: Quantum Optimization Algorithms.- Chapter 7: Quantum Algorithms.- Chapter 8: Quantum Neural Network Algorithms.- Chapter 9: Quantum Classification Algorithms.- Chapter 10: Quantum Data Processing.- Chapter 11: Quantum AI Algorithms.- Chapter 12: Quantum Solutions.- Chapter 13: Evolving Quantum Solutions.- Chapter 14: Next Steps.
About the Author :
Bhagvan Kommadi is the founder of Architect Corner, an AI startup, and he has 20 years of industry experience ranging from large-scale enterprise development to helping incubate software product startups. He has a masters degree in industrial systems engineering from Georgia Institute of Technology and a bachelors degree in aerospace engineering from the Indian Institute of Technology, Madras. He is a member of the IFX forum, Oracle JCP, and a participant in Java Community Process.
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.
