Quantum Machine Learning and Optimisation in Finance: Drive financial innovation with quantum-powered algorithms and optimisation strategies

Quantum Machine Learning and Optimisation in Finance: Drive financial innovation with quantum-powered algorithms and optimisation strategies

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 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. 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 “While this book does not focus as much on finance as other titles, it builds from the ground up the knowledge necessary to develop key algorithms in the field of quantum algorithm engineering. It approaches the subject from an application perspective, highlighting the potential of techniques that are not yet fully exploitable due to hardware limitations. It helps to build the foundational knowledge needed for those coming from other disciplines with less emphasis on physics. Undoubtedly, a highly recommended read.” Iraitz Montalban, CTO of Falcondale and Co-author of Financial Modeling Using Quantum Computing “A fantastic deep dive into quantum machine learning and optimization, covering QUBO, quantum boosting, Boltzmann machines, and more. The book balances theory with hands-on applications and offers great insights into both annealing and gate-based approaches. A must-read for those looking to explore QML in finance.” Dr. Christophe Pere, QML Researcher, Qiskit Advocate “Quantum Machine Learning and Optimization in Finance by Jack Jacquier and Oleksiy Kondratyev is one of the most well-structured books on the subject. It provides a clear progression from quantum mechanics fundamentals to advanced QML applications in finance, covering both annealing and gate-based approaches. Highly recommended for anyone exploring Quantum Machine Learning!” Anshul Saxena, PhD., Coauthor of Financial Modeling Using Quantum Computing “Quantum Machine Learning and Optimization in Finance is a game-changer for anyone exploring quantum applications in