Quantum Machine Learning with Python

1. Front Matter

   Pages i-xix

   PDF

2. Introduction to Quantum Computing

   • Santanu Pattanayak

   Pages 1-43

3. Mathematical Foundations and Postulates of Quantum Computing

   • Santanu Pattanayak

   Pages 45-94

4. Introduction to Quantum Algorithms

   • Santanu Pattanayak

   Pages 95-149

5. Quantum Fourier Transform and Related Algorithms

   • Santanu Pattanayak

   Pages 151-220

6. Quantum Machine Learning

   • Santanu Pattanayak

   Pages 221-279

7. Quantum Deep Learning

   • Santanu Pattanayak

   Pages 281-306

8. Quantum Variational Optimization and Adiabatic Methods

   • Santanu Pattanayak

   Pages 307-355

9. Back Matter

   Pages 357-361

   PDF

Quickly scale up to Quantum computing and Quantum machine learning foundations and related mathematics and expose them to different use cases that can be solved through Quantum based algorithms.This book explains Quantum Computing, which leverages the Quantum mechanical properties sub-atomic particles. It also examines Quantum machine learning, which can help solve some of the most challenging problems in forecasting, financial modeling, genomics, cybersecurity, supply chain logistics, cryptography among others.

You'll start by reviewing the fundamental concepts of Quantum Computing, such as Dirac Notations, Qubits, and Bell state, followed by postulates and mathematical foundations of Quantum Computing. Once the foundation base is set, you'll delve deep into Quantum based algorithms including Quantum Fourier transform, phase estimation, and HHL (Harrow-Hassidim-Lloyd) among others. 


You'll then be introduced to Quantum machine learning and Quantum deep learning-based algorithms, along with advanced topics of Quantum adiabatic processes and Quantum based optimization. Throughout the book, there are Python implementations of different Quantum machine learning and Quantum computing algorithms using the Qiskit toolkit from IBM and Cirq from Google Research.


What You'll Learn

• Understand Quantum computing and Quantum machine learning
  
• Explore varied domains and the scenarios where Quantum machine learning solutions can be applied
  
• Develop expertise in algorithm development in varied Quantum computing frameworks
  
• Review the major challenges of building large scale Quantum computers and applying its various techniques
  

Who This Book Is For


Machine Learning enthusiasts and engineers who want to quickly scale up to Quantum Machine Learning

• Quantum Computing
• Machine Learning
• Quantum Mechanics
• Quantum Algorithms
• Quantum Machine Learning
• Quantum Deep Learning
• Quantum Optimization

“The rigor, the mathematical detail, and the inclusion of proofs are very important contributions … . the book is well written and easy to read. Concepts, ideas, and algorithms are very well illustrated with simple examples but then also explained in exquisite mathematical detail, followed by concise yet nicely explained codification in Cirq or Qiskit.” (Santiago Escobar, Computing Reviews, October 28, 2021)

• Bangalore, India

  Santanu Pattanayak

Santanu Pattanayak works as a staff machine learning specialist at Qualcomm Corp R&D and is an author of the book “Pro Deep Learning with TensorFlow” published by Apress. He has around 12 years of work experience and has worked at GE, Capgemini, and IBM before joining Qualcomm. He graduated with a degree in electrical engineering from Jadavpur University, Kolkata and is an avid math enthusiast. Santanu has a master’s degree in data science from Indian Institute of Technology (IIT), Hyderabad. He also participates in Kaggle competitions in his spare time where he ranks in top 500. Currently, he resides in Bangalore with his wife.

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