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

Key FeaturesFind out how quantum algorithms enhance financial modeling and decision-makingImprove your knowledge of the variety of quantum machine learning and optimisation algorithmsLook into practical near-term applications for tackling real-world financial challengesPurchase of the print or Kindle book includes a free PDF eBookBook 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 learnFamiliarize yourself with analog and digital quantum computing principles and methodsExplore solutions to NP-hard combinatorial optimisation problems using quantum annealersBuild and train quantum neural networks for classification and market generationDiscover how to leverage quantum feature maps for enhanced data representationWork with variational algorithms to optimise quantum processesImplement symmetric encryption techniques on a quantum computerWho 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 ContentsThe Principles of Quantum MechanicsAdiabatic Quantum ComputingQuadratic Unconstrained Binary OptimisationQuantum BoostingQuantum Boltzmann MachineQubits and Quantum Logic GatesParameterised Quantum Circuits and Data EncodingQuantum Neural NetworkQuantum Circuit Born MachineVariational Quantum EigensolverQuantum Approximate Optimisation AlgorithmQuantum Kernels and Quantum Two-Sample TestThe Power of Parameterised Quantum CircuitsAdvanced QML ModelsBeyond NISQ

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