Quantum Computing Solutions: Solving Real-World Problems Using Quantum Computing and Algorithms

ISBN-13: 9781484265154 / Angielski / Miękka / 2020 / 300 str.

Bhagvan Kommadi

Opis

Intermediate user level

Spis treści

Part 1: Introduction

Chapter 1: Quantum Computing Solutions Overview

1.1 Real-Life Problems and Solutions

1.2 Solution Benefits

1.2.1 Automation of manual, semi manual processes

1.2.3 Cost Reduction and Improving Profit

1.2.3 Improving Efficiencies and reducing the defects

1.3 Solutions

1.3.1 Cryptography

1.3.2 Optimization

1.3.3 Cyber Security

 

Chapter 2: Mathematics behind Quantum Computing

2.1 Quantum Operators

2.2 Sets

2.3 Vectors

2.4 Matrices

2.5 Tensors

 

Part 2: Quantum Computing Basics

Chapter 3: Quantum SubSystems and Properties

3.1 Single Qubit System

3.2 Multiple Qubit System

3.3 Quantum States

3.4 Quantum Protocols

3.5 Quantum Operations

3.6 Quantum Transformations

 

Chapter 4: Quantum Information Processing Framework

4.1 Quantum Circuits

4.2 Quantum Communication

4.3 Quantum Noise

4.4 Quantum Error Correction

4.5 Limitations of Quantum Computing

4.6 Quantum Algorithms

4.6.1 Duetsch-Jozsa Algorithm

4.6.2 Simon’s Algorithm

4.6.3 Shor’s Algorithm

4.6.4 Grover’s Algorithm

4.7 Quantum Subroutines

 

Chapter 5: Quantum Simulators

5.1 Quantum Languages

5.2 Qubit Measurement

5.3 Quantum Instruction Sets

5.4 Full stack Universal Quantum Simulator

5.5 Quantum Assembly Programming

5.6 Quantum Hardware Platforms

 

Part 3: Quantum Solutions

Chapter 6: Quantum Optimization Algorithms

6.1 Approximate optimization algorithms

6.2 Combinatorial Optimization

6.3 SemiDefinite Programming

6.4 Quantum NP (BQNP)

 

Chapter 7: Quantum Algorithms

7.1 Quantum Least Squares fitting

7.2 Quantum semidefinite programming

7.3 Quantum sort

7.4 Quantum Eigen Solvers

 

Chapter 8: Quantum Neural Network Algorithms

8.1 Quantum ANN

8.2 Quantum Associative Memory

8.3 Quantum Dots

8.4 Quantum Random Access Memory

8.5 Quantum GAN

 

Chapter 9: Quantum Classification Algorithms

9.1 Classifiers

9.2 Support Vector Machines

9.3 Sparse Support Vector machines

 

Chapter 10: Quantum Data Processing

10.1 Quantum K-Means

10.2 Quantum K-Medians

10.3 Quantum Clustering

10.4 Quantum Manifold Embedding

 

Chapter 11: Quantum AI Algorithms

11.1 Quantum Probability

11.2 Quantum Walks

11.3 Quantum Search

11.4 Quantum Deep Learning

11.5 Quantum Parallelism

 

Chapter 12: Quantum Solutions

12.1 Traveling Salesman Problem

12.2 Scheduling

12.3 Fraud Detection

12.4 Distribution solutions

 

Chapter 13: Evolving Quantum Solutions

13.1 Quantum Annealing

13.2 Quantum Key Distribution

13.3 Quantum Teleportation

 

Chapter 14: Next Steps

14.1 Best Practices

14.2 Case Studies

14.3 Quantum Computing News Sources

14.3 Quantum Ware - Future

O autorze

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 network for 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.

Blurb

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