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Author:   Santanu Ganguly
Publisher:   APress
Imprint:   APress
Edition:   1st ed.
Weight:   1.075kg
ISBN:  

9781484270974

ISBN 10:   1484270975
Pages:   551
Publication Date:   30 July 2021
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Chapter 1:  IntroductionChapter Goal: Introduction to book and topics to be covered No of pages 12 Sub -Topics 1.    Rise of The Quantum Computers 2.    Learning from data: AI, ML and Deep Learning 3.    Way forward 4.    Bird’s Eye view of Quantum Machine Learning Algorithms 5.    Organisation of the book 6.    Software and Languages (Linux and Python libraries) Chapter 2: Quantum Computing & Information 1.    Chapter Goal: A comprehensive understanding of key concepts related to Quantum information science and cloud based free access options for quantum computation quantum domain with examples No of pages: 65 Sub - Topics: 2.    Basics of Quantum Computing: Qubits, Bloch sphere and gates 3.    Quantum Circuits 4.    Quantum Parallelism 5.    Quantum Computing by Annealing 6.    Quantum Computing with Superconducting qubits 7.    Other flavours of Quantum Computing 8.    Algorithms: Grover, Deutsch, Deutsch-Josza 9.    Optimisation theory 10. Hands-on exercises   Chapter 3: Quantum Information Encoding Chapter Goal: To understand how to encode data in quantum machine learning space with examples No of pages: 30 Sub - Topics: 26. Initiation and selection of data 27. Basis encoding 28. Superposition of inputs 29. Sampling Theory 30. Hamiltonian 31. Amplitude Encoding 32. Other Encoding techniques 33. Hands-on exercises   Chapter 4: QML Algorithms Chapter Goal: Understanding hardware driven algorithmic computations for quantum machine learning No of pages: 35 Sub - Topics: 34. Hardware Interface (Quantum Processors) 35. Quantum K-Means and K-Medians 36. Quantum Clustering 37. Quantum Classifiers (e.g., nearest neighbours) 38. Support Vector Machine (SVM) in quantum space 39. Hands-on exercises   Chapter 5: Inference Chapter Goal: Models and methods used in Quantum Machine Learning No of pages: 35 Sub - Topics: 40. Principal Component Analysis 41. Feature Maps 42. Linear Models 43. Probabilistic Models 44. Hands-on Exercises   Chapter 6: Training the Data Chapter Goal: Training models and techniques of Quantum Machine Learning No of pages: 105 Sub - Topics: 45. Unsupervised and supervised learning 46. Matrix inversion 47. Amplitude amplification for QML 48. Quantum optimization 49. Travelling Salesman Problem 50. Variational Algorithms 51. QAOA 52. Maxcut Problem 53. VQE (Virtual Quantum Eigensolver) 54. Varitaional Classification algorithms 55. Hands-on Exercises   Chapter 7: Quantum Learning Models Chapter Goal: Learning models and techniques of Quantum Machine Learning No of pages: 75 Sub - Topics: 56. Optimal state for learning 57. Channel State duality 58. Tomography 59. Quantum Neural Networks 60. Quantum Walk 61. Tensor Network applications 62. Hands-on Exercises   Chapter 8: Future of QML in Research and Industry Chapter Goal: Forward looking prospects of Quantum Machine Learning in industry, enterprises and opportunities No of pages: 15 Sub - Topics: 1.    Speed up that Big Data 2.    Effect of Error Correction 3.    Machine learning marries Quantum Computing 4.    QBoost 5.    Quantum Walk 6.    Mapping to hardware 7.    Hands-on Exercises References Index  

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Author Information

Santanu Ganguly has been working in the fields of quantum technologies, cloud computing, data networking, and security (on research, design, and delivery) for over 21 years. He works in Switzerland and the United Kingdom (UK) for various Silicon Valley vendors and ISPs. He has two postgraduate degrees (one in mathematics and another in observational astrophysics), and research experience and publications in nanoscale photonics and laser spectroscopy. He is currently leading global projects out of the UK related to quantum communication and machine learning, among other technologies.

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