Quantum Machine Learning and Optimisation in Finance: On the Road to Quantum Advantage

Description

Learn the principles of quantum machine learning and how to apply them While focus is on financial use cases, all the methods and techniques are transferable to other fields Purchase of Print or Kindle includes a free eBook in PDF Key FeaturesDiscover how to solve optimisation problems on quantum computers that can provide a speedup edge over classical methodsUse methods of analogue and digital quantum computing to build powerful generative modelsCreate the latest algorithms that work on Noisy Intermediate-Scale Quantum (NISQ) computersBook Description With recent advances in quantum computing technology, we finally reached the era of Noisy Intermediate-Scale Quantum (NISQ) computing. NISQ-era quantum computers are powerful enough to test quantum computing algorithms and solve hard real-world problems faster than classical hardware. Speedup is so important in financial applications, ranging from analysing huge amounts of customer data to high frequency trading. This is where quantum computing can give you the edge. Quantum Machine Learning and Optimisation in Finance shows you how to create hybrid quantum-classical machine learning and optimisation models that can harness the power of NISQ hardware. This book will take you through the real-world productive applications of quantum computing. The book explores the main quantum computing algorithms implementable on existing NISQ devices and highlights a range of financial applications that can benefit from this new quantum computing paradigm. This book will help you be one of the first in the finance industry to use quantum machine learning models to solve classically hard real-world problems. We may have moved past the point of quantum computing supremacy, but our quest for establishing quantum computing advantage has just begun! What you will learnTrain parameterised quantum circuits as generative models that excel on NISQ hardwareSolve hard optimisation problemsApply quantum boosting to financial applicationsLearn how the variational quantum eigensolver and the quantum approximate optimisation algorithms workAnalyse the latest algorithms from quantum kernels to quantum semidefinite programmingApply quantum neural networks to credit approvalsWho this book is for This book is for Quants and developers, data scientists, researchers, and students in quantitative finance. Although the focus is on financial use cases, all the methods and techniques are transferable to other areas. 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 AlgorithmThe Power of Parameterised Quantum CircuitsLooking AheadBibliography Cover Copyright Contributors Table of Contents Preface Chapter 1: The Principles of Quantum Mechanics Part I Chapter 2: Adiabatic Quantum Computing Chapter 3: Quadratic Unconstrained Binary Optimisation Chapter 4: Quantum Boosting Chapter 5: Quantum Boltzmann Machine Part II Chapter 6: Qubits and Quantum Logic Gates Chapter 7: Parameterised Quantum Circuits and Data Encoding Chapter 8: Quantum Neural Network Chapter 9: Quantum Neural Network Chapter 10: Variational Quantum Eigensolver Chapter 11: Quantum Approximate Optimisation Algorithm Chapter 12: The Power of Parameterised Quantum Circuits Chapter 13: Looking Ahead Index Other Books You Might Enjoy Packt Page