Quantum Machine Learning and Optimisation in Finance

Get a detailed introduction to quantum computing and quantum machine learning, with a focus on finance-related applications in this second editionKey FeaturesExplore updated quantum algorithms that enhance financial modeling, including advanced QML techniquesGain insights into new hybrid quantum-classical optimization strategies for NISQ systemsDiscover expanded practical applications tackling real-world financial challengesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionAs 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 learnFamiliarize yourself with expanded analog and digital quantum computing principlesSolve NP-hard optimization problems with updated quantum annealing methodsBuild and train advanced quantum neural networks for financeLeverage new quantum kernels for enhanced data representationOptimize processes using expanded variational algorithmsExplore advanced symmetric encryption techniques on quantum systemsWho this book is forThis 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.]]>