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

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

Key Features

• Explore updated quantum algorithms that enhance financial modeling, including advanced QML techniques
• Gain insights into new hybrid quantum-classical optimization strategies for NISQ systems
• Discover expanded practical applications tackling real-world financial challenges
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

As quantum machine learning (QML) evolves, this second edition builds upon the foundation of the first, providing a hands-on guide to applying advanced QML algorithms for finance using noisy intermediate-scale quantum (NISQ) systems. This edition introduces new chapters exploring quantum kernels, advanced optimization methods, and quantum neural networks, expanding beyond foundational algorithms like Shor's and Grover's to focus on real-world applications. Hybrid quantum-classical protocols remain a core focus, enabling readers to effectively combine the strengths of quantum and classical computing. Written by Antoine Jacquier, a leading researcher in stochastic analysis, and Oleksiy Kondratyev, a Quant of the Year awardee, this edition provides a hardware-agnostic perspective, balancing analog and digital quantum computing insights. Updated examples and case studies provide actionable insights into leveraging quantum for finance. By the end of this book, you’ll have gained a solid understanding of the latest developments in quantum computing for finance, enabling you to solve complex challenges and drive innovation in your work.

What you will learn

• Familiarize yourself with expanded analog and digital quantum computing principles
• Solve NP-hard optimization problems with updated quantum annealing methods
• Build and train advanced quantum neural networks for finance
• Leverage new quantum kernels for enhanced data representation
• Optimize processes using expanded variational algorithms
• Explore advanced symmetric encryption techniques on quantum systems

Who this book is for

This second edition is ideal for quants, developers, data scientists, researchers, and students in quantitative finance, as well as AI/ML experts. Prior knowledge of quantum mechanics is not required. With new content on advanced QML applications and optimization techniques, this book offers accessible yet rigorous mathematical insights for solving financial challenges.