Quantum Machine Learning and Optimisation in Finance: Drive financial innovation with quantum-powered...

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Antoine Jacquier Oleksiy Kondratyev

Quantum Machine Learning and Optimisation in Finance

Drive financial innovation with quantum-powered algorithms and optimisation strategies

Antoine Jacquier, Oleksiy Kondratyev, Alexander Lipton , Marcos López de Prado

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English

Packt Publishing Limited
31 December 2024

Operational research; Optimisation; Computer architecture & logic design

Get a detailed introduction to quantum computing and quantum machine learning, with a focus on finance-related applications

Key Features

Find out how quantum algorithms enhance financial modeling and decision-making Improve your knowledge of the variety of quantum machine learning and optimisation algorithms Look into practical near-term applications for tackling real-world financial challenges Purchase of the print or Kindle book includes a free PDF eBook

Book DescriptionAs quantum machine learning (QML) continues to evolve, many professionals struggle to apply its powerful algorithms to real-world problems using noisy intermediate-scale quantum (NISQ) hardware. This book bridges that gap by focusing on hands-on QML applications tailored to NISQ systems, moving beyond the traditional textbook approaches that explore standard algorithms like Shor's and Grover's, which lie beyond current NISQ capabilities. You’ll get to grips with major QML algorithms that have been widely studied for their transformative potential in finance and learn hybrid quantum-classical computational protocols, the most effective way to leverage quantum and classical computing systems together. The authors, Antoine Jacquier, a distinguished researcher in quantum computing and stochastic analysis, and Oleksiy Kondratyev, a Quant of the Year awardee with over 20 years in quantitative finance, offer a hardware-agnostic perspective. They present a balanced view of both analog and digital quantum computers, delving into the fundamental characteristics of the algorithms while highlighting the practical limitations of today’s quantum hardware. By the end of this quantum book, you’ll have a deeper understanding of the significance of quantum computing in finance and the skills needed to apply QML to solve complex challenges, driving innovation in your work. What you will learn

Familiarize yourself with analog and digital quantum computing principles and methods Explore solutions to NP-hard combinatorial optimisation problems using quantum annealers Build and train quantum neural networks for classification and market generation Discover how to leverage quantum feature maps for enhanced data representation Work with variational algorithms to optimise quantum processes Implement symmetric encryption techniques on a quantum computer

Who this book is forThis book is for academic researchers, STEM students, finance professionals in quantitative finance, and AI/ML experts. No prior knowledge of quantum mechanics is needed. Mathematical concepts are rigorously presented, but the emphasis is on understanding the fundamental properties of models and algorithms, making them accessible to a broader audience. With its deep coverage of QML applications for solving real-world financial challenges, this guide is an essential resource for anyone interested in finance and quantum computing.

By: Antoine Jacquier, Oleksiy Kondratyev
Foreword by: Alexander Lipton, Marcos López de Prado
Imprint: Packt Publishing Limited
Country of Publication: United Kingdom
Edition: 2nd Revised edition
Dimensions: Height: 235mm, Width: 191mm,
ISBN: 9781836209614
ISBN 10: 1836209614
Pages: 494
Publication Date: 31 December 2024
Audience: General/trade , ELT Advanced
Format: Paperback
Publisher's Status: Active

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 Quantum Kernels and Quantum Two-Sample Test The Power of Parameterised Quantum Circuits Advanced QML Models Beyond NISQ

Antoine Jacquier graduated from ESSEC Business School before obtaining a PhD in mathematics from Imperial College London. His research focuses on stochastic analysis, asymptotic methods in probability, volatility modelling, and algorithms in quantum computing. He has published about 50 papers and has co-written several books. He is also the director of the MSc in mathematics and finance at Imperial College and regularly works as a quantitative consultant for the finance industry. He has a keen interest in running and whisky. 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. Oleksiy is a Visiting Professor at the Department of Mathematics, Imperial College London, and a Research Fellow at ADIA Lab. Outside the world of finance and quantum computing, Oleksiy's passion is for sailing, in particular, offshore racing. Oleksiy holds the RYA Yachtmaster Ocean certificate of competence and is a member of the Royal Ocean Racing Club.