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

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Key benefits

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

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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.

Who is this book 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.

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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Nikhila Yeturi Jan 28, 2025

The author has an incredible ability to break down complex ideas like quantum error correction, qubits, and quantum gates into bite-sized, digestible explanations. The writing flows seamlessly, with each concept building naturally on the last, so you’re never left feeling lost. Even topics I initially thought were beyond me, like how quantum computing applies to financial systems, were explained in such a clear and intuitive way that they just clicked.What impressed me most was how the book tackled advanced concepts that many shy away from. My favorite topic was training Quantum Neural Networks (QNN) with Particle Swarm Optimization. The explanation was thorough yet approachable, making a complex subject feel surprisingly manageable. Another highlight was the way the author broke down the Estimation of the Frobenius Distance on a Quantum Computer, a topic that’s kinda dense. It was explained so clearly that I came away with a solid understanding of something I never expected to grasp so well.Beyond Quantum Computing and Finance, the book also delves into Machine Learning applications and Data Encoding techniques. These sections were written in such an engaging way that they sparked genuine curiosity, making you want to explore and learn more.If you’re looking to deepen your knowledge of quantum computing and its broader applications with Machine Learning and Finance, this is the perfect resource. Highly recommended! Read more

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Himanshu Feb 04, 2025

This book is detailed and bridges foundational Machine Learning principles with latest advancements in Quantum Computing by providing Quantum Computing analogs of ML concepts. It offers a comprehensive coverage spanning from basic linear algebra to advanced topics in Quantum Computing. It incorporates practical insights of using Quantum concepts in the field of finance for variety of problems like portfolio optimization, fraud detection, synthetic financial data generation and Quantum Monte Carlo simulations for derivative pricing. The authors have skillfully maintained the balance between Mathematical proofs for experts and explanations for abstract quantum concepts useful for novice learners. Among all the chapters, I personally found Chapter 8 on Quantum Neural Networks (QNN) fascinating because it explains how to use parameterized Quantum gates as layers in QNNs and introduces gradient based approaches like parameter shift rules for backpropagation. Focus on NISQ-era algorithms for financial use cases matching the demand for today truly set this book apart. One limitation I noticed was limited discussion on effects of noisy data on Quantum model training and how to mitigate them. Overall, as a Machine Learning Engineer with major in Mathematics and with basic knowledge of Quantum Computing, I find this book a valuable resource for understanding and applying Quantum Algorithms to Financial problems. Read more

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Joydeep Jan 20, 2025

In this book named 'Quantum Machine Learning and Optimization in Finance', the authors, Antoine Jacquier and Oleksiy Kondratyev, has skillfully demystified the cutting-edge intersection of quantum computing and financial innovation. The book offers a deep dive into quantum algorithms and their transformative potential for solving complex financial problems.Divided into two comprehensive sections—analog quantum computing and gate model quantum computing—the book explores key concepts such as quantum annealing, variational quantum eigensolvers, and quantum neural networks. These are complemented by practical applications, including portfolio optimization, credit scoring, and synthetic market data generation, making the theoretical insights immediately relevant to real-world challenges.What sets this book apart is its clarity and balance. The authors manage to maintain technical depth while ensuring accessibility for a diverse audience, ranging from finance professionals to researchers and students. By focusing on hybrid quantum-classical approaches optimized for Noisy Intermediate-Scale Quantum (NISQ) devices, the book remains both forward-looking and practical.This second edition is an indispensable guide for anyone looking to harness quantum computing's potential to revolutionize financial strategies. It's a thought-provoking and essential resource for staying at the forefront of financial technology and innovation. Read more

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Vasu Jan 31, 2025

Antoine Jacquier and Oleksiy Kondratyev's Quantum Machine Learning and Optimisation in Finance is a game-changer for anyone exploring quantum computing in financial applications. The book provides a clear and practical guide to optimization strategies like Quadratic Unconstrained Binary Optimization (QUBO), adiabatic quantum computing, and hybrid quantum-classical methods.As someone designing quantum optimization and multi-agent AI systems for trading, I found the insights on quantum algorithms, including reverse quantum annealing and quantum neural networks, particularly valuable. These concepts helped refine my approach to portfolio optimization and trading strategy enhancements.The authors strike a balance between theory and application, making complex quantum techniques accessible. For anyone working on optimization, machine learning, or AI in finance, this is a must-read resource that stands out for its clarity and practical relevance. Read more

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Pranjali Khajanji Jan 30, 2025

This is a groundbreaking book that offers an enthralling journey into the applications of Quantum Machine Learning (QML) in the finance sector. The authors have skillfully build the content from fundamental principles, postulates of quantum mechanics to advanced topics like quantum neural networks. While the book provides a comprehensive exploration, it is best suited for readers with some prior knowledge of quantum modeling and machine learning concepts. This is a intermediate-level book that serves as an excellent resource for those looking to deepen their understanding of QML in finance. Overall, it is highly recommended for finance professionals and data scientists looking to advance in this exciting field. Read more

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

What do you get with eBook?