Quantum Machine Learning (QML) is no longer just theoretical—it’s here, and it’s accessible. QML Unlocked is your gateway to understanding and applying quantum computing principles to machine learning without drowning in complex mathematics. Whether you’re a data scientist, software engineer, or simply curious about the next revolution in computing, this book provides a hands-on, code-driven approach to exploring QML.
Key Features
- Demystify quantum computing – Grasp the foundational principles behind quantum systems and why they matter for machine learning.
- Hands-on approach – Learn by doing, with minimal theory and well-structured Python code you can run yourself.
- Explore quantum hardware – Understand how Quantum Processing Units (QPUs) work, how to access them via the cloud, and what factors matter when choosing one.
- Practical business use cases – See how QML is being applied in real-world industries, from finance to logistics.
- Kickstart your quantum coding journey – Get up to speed with Qiskit, PennyLane, and other key quantum frameworks.
- Exclusive QML code samples – Access unique implementations of quantum-enhanced machine learning techniques.
- Extensive further reading – Enjoy over 100 references to expand your understanding.
Book Description
QML Unlocked opens the door for anyone looking to explore quantum machine learning in a practical and accessible way. Instead of complex formulas, the book takes you on a guided journey through the core concepts of quantum computing, real-world QPUs, and Python-based quantum programming. You’ll learn how to leverage quantum techniques for machine learning tasks, optimize models, and make sense of quantum-enhanced algorithms.
By the end of this book, you’ll have a solid foundation in QML and the skills to experiment with quantum machine learning on your own.
What You Will Learn
- The fundamentals of quantum computing and why it’s relevant for machine learning.
- An overview of today’s quantum devices and cloud-based quantum computing services.
- How to apply quantum techniques to real-world business problems.
- How to choose the right QPU and framework for your experiments.
- How to write and execute quantum machine learning models in Python.
Who This Book Is For
This book is designed for everyone—whether you're a data scientist, software engineer, ML practitioner, or simply a curious mind eager to explore quantum computing. QML Unlocked is not written for quantum specialists (though some chapters may still be of interest to them), but rather for those who want a hands-on, practical introduction to QML without needing a PhD in physics.
If you’ve ever wanted to explore the intersection of quantum computing and machine learning, this book will be your guide—helping you take your first steps into an exciting and rapidly evolving field.
Table of Contents
- Chapter 1: Quantum Computing and Machine Learning
- Chapter 2: Do Quantum Computers Really Exist?
- Chapter 3: It’s Not Trivial to Pick a QPU
- Chapter 4: Implementing Quantum Machine Learning Models with Python
- Chapter 5: The Relevance of the Preprocessing Phase
- Chapter 6: From Classical Data to Quantum States
- Chapter 7: Support Vector Classifiers
- Chapter 8: Variational Quantum Classifiers
- Chapter 9: Promising Approaches
- Chapter 10: A Journey to Implement Yourself
