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Hands-On Quantum Machine Learning With Python is your comprehensive guide to get started with Quantum Machine Learning – the use of quantum computing for the computation of machine learning algorithms.

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Here is what you will Learn from Hands-On Quantum Machine Learning with Python

1. Getting Started

1. Introduction
2. Quantum Machine Learning—Beyond The Hype
3. Quantum Computing
4. The Case for Quantum Machine Learning
5. Quiz: Introduction to Quantum Machine Learning

2. Binary Classification

1. Getting and Looking at the Dataset
2. Data Preprocessing: Missing Values
3. Data Preprocessing: Identifiers
4. Data Preprocessing: Handling Text and Categorical Attributes
5. Data Preprocessing: 4- Feature Scaling

3. Qubit and Quantum States

1. The Qubit
2. Exploring the Quantum States
3. A Visual Exploration of the Qubit State
4. Bypassing the Normalization
5. Exploring the Observer Effect

4. Probabilistic Binary Classifier

1. Towards Naïve Bayes
2. Bayes’ Theorem
3. Gaussian Naïve Bayes
4. Quiz: Probabilistic Binary Classifier

5. Working with Qubits

1. You Don’t Need to Be a Mathematician
2. A Reimplementation of OR
3. The Measured Qubit
4. Quantumic Math
5. When to Differentiate State |0> From State |1>
6. Gamble with Quantum Computing

6. Working with Multiple Qubits

1. Hands-On Introduction to Quantum Entanglement
2. Implementing the CNOT gate
3. The Equation Einstein Could Not Believe
4. The Two Qubit States and Their Transformation
5. Calculating the Transformation Matrix

7. Quantum Naïve Bayes

1. More on Naïve Bayes
2. Pre-Processing
3. PQC
4. Calculating the Posterior Probability
5. Post-processing

8. Quantum Computing Is Different

1. The No-Cloning Theorem
2. How to Solve a Problem with Quantum Computing
3. Depicting the Transformation O-gate
4. Deutsch’s Algorithm
5. The Quantum Oracle, Demystified

9. Quantum Bayesian Networks

1. Introduction to Quantum Bayesian Networks
2. Bayesian Networks
3. Composing Quantum Computing Controls
4. The CCNOT-gate
5. Circuit Implementation

10. Bayesian Inference

1. Introduction to Bayesian Inference
2. Estimating a Single Data Point
3. Calculating the log‐likelihood when ignoring the missing data
4. Estimating a Variable

11. The World Is Not a Disk

1. The Qubit Phase
2. Two Different Qubit States
3. The Different States in the Bloch Sphere
4. Visualizing the Invisible Qubit Phase

12. Working with the Qubit Phase

1. Using Grover’s Algorithm
2. Basic Amplitude Amplification
3. Two-qubit Amplification
4. The Two‐qubit Grover Searching |10>
5. Quiz: Working with the Qubit Phase

13. Search for Relatives

1. The Convenience Function and Probabilities for Relatives
2. Turning the Problem into a Circuit
3. The Oracle and Amplifier Functions
4. The Search Algorithm
5. Multiple Results

14. Sampling

1. Forward Sampling
2. Bayesian Rejection Sampling
3. Quantum Rejection Sampling
4. The Amplifier Function
5. Quiz: Sampling

16. APPENDIX

1. Configuring Your Quantum Machine Learning Workstation
2. Configuring Ubuntu for Quantum Machine Learning with Python
3. How to Set Up JupyterLab for Quantum Computing on windows