Quantum Machine Learning with Python: Using Cirq from Google Research and IBM Qiskit

Quantum Machine Learning With Python

Chapter 1: Introduction to Quantum Mechanics and Quantum Computing

Chapter Goal: Introduce the concept of Quantum mechanics and Quantum computing to the readers

No of pages 50-60

Sub-Topics

1. Introduction to Quantum computing

2. Quantum bit and its realization

3. Quantum superposition and Quantum entanglement

4. Bloch Sphere representation of Qubit

5. Stern Gerlach Experiment

6. Bell State

7. Dirac Notations

8. Single Qubit Gates

9. Multiple Qubit Gates

10. Quantum No Cloning Theorem

11. Measurement in different basis

12. Quantum Teleportation

13. Quantum parallelism with Deuth Jozsa

14. Reversibility of quantum computing

Chapter 2: Mathematical Foundations and Postulates of Quantum Computing

Chapter Goal: Lays the mathematical foundation along with the postulates of Quantum computing

No of pages 50-60

Sub -Topics

1. Topics from Linear algebra

2. Pauli Operators

3. Linear Operators and their properties

4. Hermitian Operators

5. Normal Operators

6. Unitary Operators

7. Spectral Decomposition

8. Linear Operators on Tensor Product of Vectors

9. Exponential Operator

10. Commutator Anti commutator Operator

11. Postulates of Quantum Mechanics

12. Measurement Operators

13. Heisenberg Uncertainty Principle

14. Density Operators and Mixed States

15. Solovay-Kitaev Theorem and Universality of Quantum gates

Chapter 3: Introduction to Quantum Algorithms

Chapter Goal: Introduce to the readers Quantum algorithms to express the Quantum computing supremacy over classical computation

No of pages: 70-80

Sub - Topics:

1. Introduction to Cirq and Qiskit

2. Bell State creation and measurement in Cirq and qiskit

3. Quantum teleportation Implementation

4. Quantum Random Number generator

5. Deutsch Jozsa Implementation

8. Hadamard Sampling

6. Bernstein Vajirani Algorithm Implementation

7. Bell's Inequality Implementation

8. Simon's Algorithm of secret string search Implementation

9 Grover's Algorithm Implementation

10. Algorithmic complexity in Quantum and Classical computing paradigm

Chapter 4: Quantum Fourier Transform Related Algorithms

Goal: Introduce to the readers Quantum Fourier related algorithms

No of pages: 60-70

Sub - Topics:

1. Fourier Series

2. Fourier Transform

3. Discrete Fourier Transform

4. Quantum Fourier Transform(QFT)

5. QFT implementation

6. Hadamard Transform as Fourier Transform

Author: Santanu Pattanayak
Publisher: Apress
Published: 05/03/2021
Pages: 361
Binding Type: Paperback
Weight: 1.46lbs
Size: 10.00h x 7.00w x 0.79d
ISBN13: 9781484265215
ISBN10: 1484265211
BISAC Categories:
- Computers | Programming | Open Source
- Computers | Artificial Intelligence | General

About the Author

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.