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[Download] "Quantum Computing Solutions" by Bhagvan Kommadi # eBook PDF Kindle ePub Free

Quantum Computing Solutions

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eBook details

  • Title: Quantum Computing Solutions
  • Author : Bhagvan Kommadi
  • Release Date : January 20, 2020
  • Genre: Databases,Books,Computers & Internet,Science & Nature,Physics,Programming,
  • Pages : * pages
  • Size : 8395 KB

Description

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 optimization and 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.
You will:Understand the mathematics behind quantum computingKnow the solution benefits, such as automation, cost reduction, and efficienciesBe familiar with the quantum subsystems and properties, including states, protocols, operations, and transformationsBe aware of the quantum classification algorithms: classifiers, and support and sparse support vector machinesUse AI algorithms, including probability, walks, search, deep learning, and parallelism


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