Quantum Machine Learning and Optimisation in Finance - Second Edition

<p><strong>Get a detailed introduction to quantum computing and quantum machine learning with a focus on finance-related applications</strong></p><p><strong><strong>Key Features:</strong></strong></p><p>- Find out how quantum algorithms enhance financial modeling and decision-making</p><p>- Improve your knowledge of the variety of quantum machine learning and optimisation algorithms</p><p>- Look into practical near-term applications for tackling real-world financial challenges</p><p>- Purchase of the print or Kindle book includes a free PDF eBook</p><p><strong><strong>Book Description:</strong></strong></p><p>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.</p><p>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.</p><p>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.</p><p>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.</p><p><strong><strong>What You Will Learn:</strong></strong></p><p>- Familiarize yourself with analog and digital quantum computing principles and methods</p><p>- Explore solutions to NP-hard combinatorial optimisation problems using quantum annealers</p><p>- Build and train quantum neural networks for classification and market generation</p><p>- Discover how to leverage quantum feature maps for enhanced data representation</p><p>- Work with variational algorithms to optimise quantum processes</p><p>- Implement symmetric encryption techniques on a quantum computer</p><p><strong><strong>Who this book is for:</strong></strong></p><p>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.</p><p><strong><strong>Table of Contents</strong></strong></p><p>- The Principles of Quantum Mechanics</p><p>- Adiabatic Quantum Computing</p><p>- Quadratic Unconstrained Binary Optimisation</p><p>- Quantum Boosting</p><p>- Quantum Boltzmann Machine</p><p>- Qubits and Quantum Logic Gates</p><p>- Parameterised Quantum Circuits and Data Encoding</p><p>- Quantum Neural Network</p><p>- Quantum Circuit Born Machine</p><p>- Variational Quantum Eigensolver</p><p>- Quantum Approximate Optimisation Algorithm</p><p>- Quantum Kernels and Quantum Two-Sample Test</p><p>- The Power of Parameterised Quantum Circuits</p><p>- Advanced QML Models</p><p>- Beyond NISQ</p>