LEADER 00000nam a2200457 i 4500 
001    978-1-4842-6516-1 
003    DE-He213 
005    20210317170808.0 
006    m     o  d         
007    cr nn 008maaau 
008    201220s2020    cau     s         0 eng d 
020    9781484265161|q(electronic bk.) 
020    9781484265154|q(paper) 
024 7  10.1007/978-1-4842-6516-1|2doi 
040    GP|cGP|erda|dAS 
041 0  eng 
050  4 QA76.889 
082 04 006.3843|223 
100 1  Kommadi, Bhagvan,|eauthor 
245 10 Quantum computing solutions :|bsolving real-world problems
       using quantum computing and algorithms /|cby Bhagvan 
264  1 Berkeley, CA :|bApress :|bImprint: Apress,|c2020 
300    1 online resource (xvii, 300 pages) :|billustrations, 
       digital ;|c24 cm 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
347    text file|bPDF|2rda 
505 0  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 
520    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 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 
650  0 Quantum computing 
650  0 Quantum computers 
650 14 Big Data 
650 24 Python 
650 24 Quantum Computing 
710 2  SpringerLink (Online service) 
773 0  |tSpringer Nature eBook 
856 40 |uhttps://doi.org/10.1007/978-1-4842-6516-1|zeBook