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
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.
