Quantum Machine Learning and Optimisation in Finance: On the Road to Quantum Advantage, 1st Edition

While focus is on financial use cases, all the methods and techniques are transferable to other fields\n\nKey Features\nDiscover how to solve optimisation problems on quantum computers that can provide a speedup edge over classical methods\nUse methods of analogue and digital quantum computing to build powerful generative models\nCreate the latest algorithms that work on Noisy Intermediate-Scale Quantum (NISQ) computers\nBook Description\nWith 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.\nSpeedup 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.\nThis 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.\nThis 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!\nWhat you will learn\nTrain parameterised quantum circuits as generative models that excel on NISQ hardware\nSolve hard optimisation problems\nApply quantum boosting to financial applications\nLearn how the variational quantum eigensolver and the quantum approximate optimisation algorithms work\nAnalyse the latest algorithms from quantum kernels to quantum semidefinite programming\nApply quantum neural networks to credit approvals\nWho this book is for\nThis 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.\nQuantum Machine Learning and Optimisation in Finance: On the Road to Quantum Advantage 1st Edition is written by Antoine Jacquier, Oleksiy Kondratyev, Alexander Lipton, Marcos López de Prado and published by Packt Publishing. ISBNs for Quantum Machine Learning and Optimisation in Finance are 9781801817875, 1801817871 and the print ISBNs are 9781801813570, 1801813574.