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Hands-On Quantum Machine Learning with Python

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Getting Started

Introduction Quantum Machine Learning—Beyond The Hype Quantum Computing The Case for Quantum Machine Learning Quiz: Introduction to Quantum Machine Learning

Binary Classification

Getting and Looking at the Dataset Data Preprocessing: Missing Values Data Preprocessing: Identifiers Data Preprocessing: Handling Text and Categorical Attributes Data Preprocessing: 4- Feature Scaling Data Preprocessing: Training and Testing Baseline Classifier Evaluation Classifier Measures Unmask the Hypocrite Classifier Visualization of Hypocrite Classifiers Performance Evaluation of Hypocrite Classifier Quiz: Binary Classification

Qubit and Quantum States

The Qubit Exploring the Quantum States A Visual Exploration of the Qubit State Bypassing the Normalization Exploring the Observer Effect Parameterized Quantum Circuit Variational Hybrid Quantum Classical Algorithm Implementing of Variational Hybrid Quantum Classical Algorithm The Weighting Pre‐processing Quiz: Qubit and Quantum States

Probabilistic Binary Classifier

Towards Naïve Bayes Bayes' Theorem Gaussian Naïve Bayes Quiz: Probabilistic Binary Classifier

Working with Qubits

You Don't Need to Be a Mathematician A Reimplementation of OR The Measured Qubit Quantumic Math When to Differentiate State |0> From State |1>Gamble with Quantum Computing Probabilities and the Qubit States Rotating the Qubit State Quiz: Working with Qubits

Working with Multiple Qubits

Hands-On Introduction to Quantum Entanglement

Implementing the CNOT gate

The Equation Einstein Could Not Believe

The Two Qubit States and Their Transformation

Calculating the Transformation Matrix

Quantum Programming for Non-mathematicians

Representing a Marginal Probability

Calculating Joint Probability

The Controlled RY Gate

Calculating Conditional Probability

Applying the Prior

Calculating the Posterior

Quiz: Working with Multiple Qubits

Quantum Naïve Bayes

More on Naïve Bayes Pre-Processing PQC Calculating the Posterior Probability Post-processing Quiz: Quantum Naïve Bayes

Quantum Computing Is Different

The No-Cloning Theorem How to Solve a Problem with Quantum Computing Depicting the Transformation O-gate Deutsch's Algorithm The Quantum Oracle, Demystified Magician-created Code Quiz: Working with Multiple Qubits

Quantum Bayesian Networks

Introduction to Quantum Bayesian Networks

Bayesian Networks

Composing Quantum Computing Controls

Circuit Implementation

Executing the Circuit

Quiz: Working with Multiple Qubits

Bayesian Inference

Introduction to Bayesian Inference Estimating a Single Data Point Calculating the log‐likelihood when ignoring the missing data Estimating a Variable Calculating the Probabilities Related to the Ticket Class Calculating the Parameters Calculating the Marginal Probability and Log-likelihood Calculating New Parameters Predict Survival Quiz: Bayesian Inference

The World Is Not a Disk

The Qubit Phase Two Different Qubit States The Different States in the Bloch Sphere Visualizing the Invisible Qubit Phase The Z-gate Multi-qubit Phase The Controlled Z-gate Phase Kickback The Bloch Spheres of the State | +->Quantum Amplitudes and Probabilities Quiz: The World Is Not a Disk

Working with the Qubit Phase

Using Grover's Algorithm Basic Amplitude Amplification Two-qubit Amplification The Two‐qubit Grover Searching |10>Quiz: Working with the Qubit Phase

Search for Relatives

The Convenience Function and Probabilities for Relatives Turning the Problem into a Circuit The Oracle and Amplifier Functions The Search Algorithm Multiple Results Quiz: Searching for Relatives

Sampling

Forward Sampling Bayesian Rejection Sampling Quantum Rejection Sampling The Amplifier Function Quiz: Sampling

Conclusion

APPENDIX

Configuring Your Quantum Machine Learning Workstation Configuring Ubuntu for Quantum Machine Learning with Python How to Set Up JupyterLab for Quantum Computing on Windows

Course Assessment

Quantum Machine Learning in Python

Quiz: Working with Multiple Qubits

Test the concepts you have learned in this chapter.

1

What is a Bayesian network?

A)

Bayesian networks are qualitative models that model knowledge about an uncertain domain.

B)

Bayesian networks are quantitative models that model knowledge about an uncertain domain.

C)

Bayesian networks are probabilistic models that model knowledge about an uncertain domain.

D)

None of the above

Question 1 of 60 attempted

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