QML Unlocked

From Curiosity to Capability in Quantum Machine Learning
By Javier Mancilla Montero

About the Book

QML Unlocked is a practical guide that bridges the gap between curiosity and capability in Quantum Machine Learning (QML). It explores fundamental quantum computing principles, real-world applications of quantum machine learning models, and hands-on Python implementations using quantum frameworks.

This repository contains accompanying code examples, figures, and references used in the book. If you're diving into QML, this repo will help you experiment with quantum algorithms and techniques.

Table of Contents

The book covers a wide range of topics, including:

1. Quantum Computing and Machine Learning
2. Quantum Processing Units (QPUs) and Cloud Access
3. Choosing the Right Quantum Hardware
4. Implementing QML Models with Python (Code Available in Repo)
5. The Importance of Preprocessing in QML (Code Available in Repo)
6. Encoding Classical Data into Quantum States
7. Quantum Support Vector Classifiers (QSVC) (Code Available in Repo)
8. Variational Quantum Classifiers (VQC) (Code Available in Repo)
9. Promising Quantum Approaches (QUBO-based SVM, Reservoir Computing, etc.) (Code Available in Repo)
10. End-to-End Implementation of Quantum ML Solutions (Code Available in Repo)

For more details, the full book is available on Amazon.

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Code in This Repository

This repo contains the Python code implementations found in key chapters:

• Chapter 4: Implementing QML Models with Python
• Chapter 5: The Importance of Preprocessing
• Chapter 7: Quantum Support Vector Classifiers (QSVC)
• Chapter 8: Variational Quantum Classifiers (VQC)
• Chapter 9: Promising Quantum Approaches
• Chapter 10: End-to-end implementation

Additionally, a References and Figures.md file includes URLs and citations for all figures and external references used in the book.

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Citation & Attribution

If you use this repository or any of its code in your research or projects, please give proper attribution:

@book{Mancilla2025QMLUnlocked, author = {Javier Mancilla Montero}, title = {QML Unlocked: From Curiosity to Capability in Quantum Machine Learning}, year = {2025}, publisher = {Amazon} }

Feel free to share feedback, open issues, or contribute.

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Connect & Contribute

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