English | 2021 | ISBN: NA | 435 Pages | PDF | 5.2 MB
Hands-On Quantum Machine Learning With Python is your comprehensive guide to get started with Quantum Machine Learning - the use of quantum computing for computation of machine learning algorithms.
Hands-On Quantum Machine Learning With Python You're interested in quantum computing and machine learning... ...But you don't know how to get started? Let me help!
Whether you just get started with quantum computing and machine learning or you're already a senior machine learning engineer, 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.
Quantum computing promises to solve problems intractable with current computing technologies. But is it fundamentally different and asks us to change the way we think.
Hands-On Quantum Machine Learning With Python strives to be the perfect balance between theory taught in a textbook and the actual hands-on knowledge you’ll need to implement real-world solutions.
Inside this book, you will learn the basics of quantum computing and machine learning in a practical and applied manner.
Hands-On Quantum Machine Learning With Python provides a no-nonsense teaching style guaranteed to cut through all the cruft and help you master Quantum Machine Learning
Hands-on tutorials (with lots of code) that not only show you the concepts of quantum computing and the algorithms behind machine learning but their implementations as well.
Inside Hands-On Quantum Machine Learning With Python, you'll learn the basics of machine learning and quantum computing.
You'll learn how to create parameterized quantum circuits and variational hybrid quantum-classical algorithms that solve classification tasks.
Learn about quantum superposition, entanglement, and interference and how you can use it to solve problems intractable for classical computers.
This book offers a practical, hands-on exploration of quantum machine learning. Rather than working through tons of theory, we will build up practical intuition about the core concepts. We will acquire the exact knowledge we need to solve practical examples with lots of code. Step by step, you will extend your knowledge and learn how to solve new problems.
Of course, we will do some math. Of course, we will cover a little physics. But I don’t expect you to hold a degree in any of these two fields. We will go through all the concepts we need. While this includes some mathematical notation and formulae, we keep it at the minimum required to solve our practical problems.
The theoretical foundation of quantum machine learning may appear overwhelming at first sight.
Be assured, when put into the right context and when explained conceptually, it is not as hard as it sounds. And this is what’s inside Hands-On Quantum Machine Learning With Python.
Is this book right for me?
You don't need to be a mathematician.
You don't need to be a physicist, either.
This book is for developers, programmers, students, and researchers who have at least some programming experience and who want to become proficient in quantum machine learning. Don’t worry if you’re just getting started with quantum computing and machine learning. We will begin with the very basics. We don’t assume prior knowledge of machine learning or quantum computing. You will not get left behind.
The time you’ll save by reading through Hands-On Quantum Machine Learning With Python will more than pay for itself.
For all examples inside Hands-On Quantum Machine Learning With Python, we use Python as our programming language. Python is easy to learn. Its simple syntax allows you to concentrate on learning quantum machine learning, rather than spending your time with the specificities of the language.
The most important library we use is Qiskit. It is IBM's quantum computing SDK. Qiskit is open-source. It provides tools for creating and manipulating quantum programs and running them on prototype quantum devices on IBM Quantum Experience or on simulators on your local computer.
For all the machine learning parts, we will use Scikit-Learn. Scikit-learn is the most useful library for machine learning in Python. It contains a range of supervised and unsupervised learning algorithms. Scikit-learn builds upon a range of other very useful libraries, such as:
NumPy: Work with n-dimensional arrays SciPy: Fundamental library for scientific computing Matplotlib: Comprehensive 2D/3Dbplotting IPython: Enhanced interactive console Sympy: Symbolic mathematics Pandas: Data structures and analysis Algorithms
Inside this book, we will learn how to create actual algorithms from the scratch, such as: