Descript 
1 online resource (xvii, 300 pages) : illustrations, digital ; 24 cm 

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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 

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 stepbystep detail. The book presents code samples based on reallife problems in a variety of industries, such as risk assessment and fraud detection in banking. In pharma, you will look at drug discovery and proteinfolding 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 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 
Host Item 
Springer Nature eBook

Subject 
Quantum computing


Quantum computers


Big Data


Python


Quantum Computing

Alt Author 
SpringerLink (Online service)

