Quantum Machine Learning and Optimisation in Finance - Second Edition
by Antoine Jacquier, Oleksiy Kondratyev
Overview
Quantum Machine Learning and Optimisation in Finance introduces you to cutting-edge quantum-powered algorithms tailored for financial applications, providing a practical guide to leveraging quantum computing for solving complex financial challenges. You will explore approaches for NISQ systems and hybrid quantum-classical computational techniques, empowering you to implement advanced quantum strategies in your work.
What this Book will help me do
- Understand the foundational principles of both analog and digital quantum computing.
- Solve NP-hard combinatorial optimisation problems using quantum annealers.
- Build and train quantum neural networks for applications in classification and market simulation.
- Leverage quantum feature maps for enhanced data representation and analysis.
- Implement variational quantum algorithms to improve quantum computational processes.
Author(s)
Antoine Jacquier is a leading academic in quantum computing and mathematical finance, with extensive experience in stochastic processes. Oleksiy Kondratyev is a respected practitioner with over 20 years in quantitative finance, awarded as Quant of the Year. Together, they bring a wealth of expertise and real-world insights to provide an enriched learning experience for readers.
Who is it for?
This book is ideal for finance professionals such as quants and AI/ML experts eager to explore quantum computing's potential in the finance domain. Even without prior quantum mechanics knowledge, readers with a solid STEM background can grasp the concepts presented. This comprehensive guide aims to aid practitioners looking to solve real financial challenges using quantum technologies. It also caters to academics and students aiming to stay ahead in cutting-edge research.
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