Book Image

Quantum Machine Learning and Optimisation in Finance

By : Antoine Jacquier, Oleksiy Kondratyev
Book Image

Quantum Machine Learning and Optimisation in Finance

By: Antoine Jacquier, Oleksiy Kondratyev

Overview of this book

With recent advances in quantum computing technology, we finally reached the era of Noisy Intermediate-Scale Quantum (NISQ) computing. NISQ-era quantum computers are powerful enough to test quantum computing algorithms and solve hard real-world problems faster than classical hardware. Speedup is so important in financial applications, ranging from analysing huge amounts of customer data to high frequency trading. This is where quantum computing can give you the edge. Quantum Machine Learning and Optimisation in Finance shows you how to create hybrid quantum-classical machine learning and optimisation models that can harness the power of NISQ hardware. This book will take you through the real-world productive applications of quantum computing. The book explores the main quantum computing algorithms implementable on existing NISQ devices and highlights a range of financial applications that can benefit from this new quantum computing paradigm. This book will help you be one of the first in the finance industry to use quantum machine learning models to solve classically hard real-world problems. We may have moved past the point of quantum computing supremacy, but our quest for establishing quantum computing advantage has just begun!
Table of Contents (4 chapters)

1
The Principles of Quantum Mechanics

Quantum mechanics is a framework for the development of physical theories; it is not itself a physical theory  [80]. Actual physical theories are built upon a foundation of quantum mechanics. This is why quantum mechanics plays such an important role in all natural sciences. Information theory is no exception and also derives inspiration from the ideas and methods of quantum mechanics.

Understanding quantum computing requires some familiarity with the basic principles of quantum mechanics. This book does not assume any prior knowledge of quantum mechanics and provides all the necessary definitions and explanations when needed. At the same time, the reader is encouraged to learn more about this fascinating subject at the level of mathematical formalism that she is comfortable with. Out of the extensive universe of textbooks on quantum mechanics that provide an introduction to this discipline, it is necessary to mention the classical book by Landau and Lifshitz  [182] as well as the equally classical book on quantum computing by Nielsen and Chuang  [223], which covers the most relevant aspects of quantum mechanics from the quantum computing perspective. For someone taking their first steps in quantum computing who would like to get the overall picture and some historical perspective, the excellent book by Bernhardt  [32] provides both without the heavy usage of complex mathematical apparatus. Readers looking for a more formal modern take on the subject of quantum mechanics may find it in the book by Robinett   [249]. The practical aspects of quantum computing are covered in great detail in the book by Sutor  [278], and anyone looking for a python quantum computing programming textbook will find it in the work by Loredo  [195].