Wow! eBook
Quantum Machine Learning and Optimisation in Finance: On the Road to Quantum Advantage
Author(s): Antoine Jacquier (Author), Oleksiy Kondratyev (Author)
- Publisher: Packt Publishing
- Publication Date: 31 Oct. 2022
- Language: English
- Print length: 442 pages
- ISBN-10: 1801813574
- ISBN-13: 9781801813570
Book Description
Learn the principles of quantum machine learning and how to apply them
While focus is on financial use cases, all the methods and techniques are transferable to other fields
Purchase of Print or Kindle includes a free eBook in PDF
Key Features
- Discover how to solve optimisation problems on quantum computers that can provide a speedup edge over classical methods
- Use methods of analogue and digital quantum computing to build powerful generative models
- Create the latest algorithms that work on Noisy Intermediate-Scale Quantum (NISQ) computers
Book Description
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!
What you will learn
- Train parameterised quantum circuits as generative models that excel on NISQ hardware
- Solve hard optimisation problems
- Apply quantum boosting to financial applications
- Learn how the variational quantum eigensolver and the quantum approximate optimisation algorithms work
- Analyse the latest algorithms from quantum kernels to quantum semidefinite programming
- Apply quantum neural networks to credit approvals
Who this book is for
This book is for Quants and developers, data scientists, researchers, and students in quantitative finance. Although the focus is on financial use cases, all the methods and techniques are transferable to other areas.
Table of Contents
- The Principles of Quantum Mechanics
- Adiabatic Quantum Computing
- Quadratic Unconstrained Binary Optimisation
- Quantum Boosting
- Quantum Boltzmann Machine
- Qubits and Quantum Logic Gates
- Parameterised Quantum Circuits and Data Encoding
- Quantum Neural Network
- Quantum Circuit Born Machine
- Variational Quantum Eigensolver
- Quantum Approximate Optimisation Algorithm
- The Power of Parameterised Quantum Circuits
- Looking Ahead
- Bibliography
Editorial Reviews
Review
“Jacquier and Kondratyev, two of the strongest quants of their generation, have written a remarkable book about quantum computers and their applications in finance, emphasizing practical aspects of quantum machine learning and optimisation.”
—
Alexander Lipton and Marcos López de Prado, ADIA
“This book reviews different types of quantum computers as well as some important principles and algorithms. Quantum Machine Learning and Optimisation in Finance is a great book to learn more about these two important applications of quantum computing.”
—
Dr. Ray O. Johnson, CEO Technology Innovation Institute
“The book is a wonderful guide for quantum computing scientists interested in understanding the applications of quantum computing in quantitative finance and finance professionals looking to explore the new computational tools.”
— Marco Paini, Director, Technology Partnerships Europe at Rigetti Computing
“Jacquier and Kondratyev succeed in leading the reader on a journey through both foundational concepts and selected research material in a very pedagogical and pleasant way. By studying the book, readers will be well equipped to understand, formulate and attack the main challenges of current quantum computing experiments applied to financial problems.”
— Davide Venturelli, Ph.D., Associate Director of Quantum Computing, USRA
“Recent surveys of UK business leaders suggest that 94% believe quantum computing will impact their organisation or sector significantly, and 72% intend to start strategic planning or create a pilot team by 2024. Only 6% have development teams in place today. In this book, Antoine and Oleksiy have created a toolkit to help focus development teams within the finance sector and beyond on the core areas of understanding and impact of quantum computing.”
—
Dr. Michael N Cuthbert, Director, National Quantum Computing Centre]
About the Author
Antoine Jacquier obtained his PhD in 2010 in Mathematics from Imperial College London, where his research was focused on large deviations and asymptotic methods for stochastic volatility. Over the past 10 years, he has been working on stochastic analysis and volatility modelling, publishing about 50 papers and co-writing several books. He is also the Head of the MSc in Mathematics and Finance at Imperial College and regularly works as a quantitative consultant for the Finance industry.
Oleksiy Kondratyev obtained his PhD in Mathematical Physics from the Institute for Mathematics, National Academy of Sciences of Ukraine, where his research was focused on studying phase transitions in quantum lattice systems. Oleksiy has over 20 years of quantitative finance experience, primarily in banking. He was recognised as Quant of the Year 2019 by Risk magazine and joined Abu Dhabi Investment Authority as a Quantitative Research & Development Lead in the summer of 2021.
