Quantum machine learning : an applied approach : the theory and application of quantum machine learning in science and industry
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Bibliographic Information
Quantum machine learning : an applied approach : the theory and application of quantum machine learning in science and industry
Apress, 2021
- pbk.
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Description and Table of Contents
Description
Know how to adapt quantum computing and machine learning algorithms. This book takes you on a journey into hands-on quantum machine learning (QML) through various options available in industry and research.
The first three chapters offer insights into the combination of the science of quantum mechanics and the techniques of machine learning, where concepts of classical information technology meet the power of physics. Subsequent chapters follow a systematic deep dive into various quantum machine learning algorithms, quantum optimization, applications of advanced QML algorithms (quantum k-means, quantum k-medians, quantum neural networks, etc.), qubit state preparation for specific QML algorithms, inference, polynomial Hamiltonian simulation, and more, finishing with advanced and up-to-date research areas such as quantum walks, QML via Tensor Networks, and QBoost.
Hands-on exercises from open source libraries regularly used today in industry and research are included, such as Qiskit, Rigetti's Forest, D-Wave's dOcean, Google's Cirq and brand new TensorFlow Quantum, and Xanadu's PennyLane, accompanied by guided implementation instructions. Wherever applicable, the book also shares various options of accessing quantum computing and machine learning ecosystems as may be relevant to specific algorithms.
The book offers a hands-on approach to the field of QML using updated libraries and algorithms in this emerging field. You will benefit from the concrete examples and understanding of tools and concepts for building intelligent systems boosted by the quantum computing ecosystem. This work leverages the author's active research in the field and is accompanied by a constantly updated website for the book which provides all of the code examples.
What You will Learn
Understand and explore quantum computing and quantum machine learning, and their application in science and industry
Explore various data training models utilizing quantum machine learning algorithms and Python libraries
Get hands-on and familiar with applied quantum computing, including freely available cloud-based access
Be familiar with techniques for training and scaling quantum neural networks
Gain insight into the application of practical code examples without needing to acquire excessive machine learning theory or take a quantum mechanics deep dive
Who This Book Is For
Data scientists, machine learning professionals, and researchers
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
by "Nielsen BookData"
