Quantum Machine Learning and Optimisation in Finance - Second Edition

Description

Get a detailed introduction to quantum computing and quantum machine learning, with a focus on finance-related applications Key FeaturesFind 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 Book Description As 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 for This 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.