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.
