Quantum Machine Learning and Optimisation in Finance - Second Edition : Drive financial innovation with quantum-powered algorithms and optimisation strategies
Language: English
Published by Packt Publishing, 2024
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
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Seller Inventory # 9781836209614
- Title
- Quantum Machine Learning and Optimisation in Finance - Second Edition : Drive financial innovation with quantum-powered algorithms and optimisation strategies
- Author
- Antoine Jacquier
- Publisher
- Packt Publishing
- Publication year
- 2024
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1836209614
- ISBN 13
- 9781836209614
- Edition
- 2nd Edition
- Item weight
- 913 grams
- Dimensions
- 235x191x27 mm
Get a detailed introduction to quantum computing and quantum machine learning, with a focus on finance-related applications
Key Features
- 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
- Purchase of the print or Kindle book includes a free PDF eBook
Book 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.
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
Who this book is 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.
Table of Contents
- The Principles of Quantum Mechanics
- Adiabatic Quantum Computing
- Quadratic Unconstrained Binary Optimisation
- Quantum Boosting
- Quantum Boltzmann Machine
- Qubits and Quantum Logic Gates
- Parameterised Quantum Circuits and Data Encoding
- Quantum Neural Network
- Quantum Circuit Born Machine
- Variational Quantum Eigensolver
- Quantum Approximate Optimisation Algorithm
- Quantum Kernels and Quantum Two-Sample Test
- The Power of Parameterised Quantum Circuits
- Advanced QML Models
- Beyond NISQ
"Synopsis" may belong to another edition of this title.
About the Author
Antoine Jacquier graduated from ESSEC Business School before obtaining a PhD in mathematics from Imperial College London. His research focuses on stochastic analysis, asymptotic methods in probability, volatility modelling, and algorithms in quantum computing. He has published about 50 papers and has co-written several books. He is also the director of the MSc in mathematics and finance at Imperial College and regularly works as a quantitative consultant for the finance industry. He has a keen interest in running and whisky.
Oleksiy Kondratyev obtained his PhD in mathematical physics from the Institute for Mathematics, National Academy of Sciences of Ukraine, where his research was focused on studying phase transitions in quantum lattice systems. Oleksiy has over 20 years of quantitative finance experience, primarily in banking. He was recognised as Quant of the Year 2019 by Risk magazine. Oleksiy is a Visiting Professor at the Department of Mathematics, Imperial College London, and a Research Fellow at ADIA Lab. Outside the world of finance and quantum computing, Oleksiy's passion is for sailing, in particular, offshore racing. Oleksiy holds the RYA Yachtmaster Ocean certificate of competence and is a member of the Royal Ocean Racing Club.
"About the title" may belong to another edition of this title.
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|---|---|---|
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