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

Jacquier Antoine

Alexei Kondratyev

AU$24.99 per month

4.6 (19 Ratings)

Paperback Oct 2022 442 pages 1st Edition

eBook

AU$45.99 ~~AU$66.99~~

Renews at AU$24.99p/m

Jacquier Antoine

Alexei Kondratyev

AU$24.99 per month

4.6 (19 Ratings)

Paperback Oct 2022 442 pages 1st Edition

eBook

AU$45.99 ~~AU$66.99~~

Renews at AU$24.99p/m

eBook

AU$45.99 ~~AU$66.99~~

Renews at AU$24.99p/m

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Key benefits

Discover how to solve optimisation problems on quantum computers that can provide a speedup edge over classical methods
Use methods of analogue and digital quantum computing to build powerful generative models
Create the latest algorithms that work on Noisy Intermediate-Scale Quantum (NISQ) computers

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!

Who is this book 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.

What you will learn

Train parameterised quantum circuits as generative models that excel on NISQ hardware
Solve hard optimisation problems
Apply quantum boosting to financial applications
Learn how the variational quantum eigensolver and the quantum approximate optimisation algorithms work
Analyse the latest algorithms from quantum kernels to quantum semidefinite programming
Apply quantum neural networks to credit approvals

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Jason Saroni Dec 31, 2022

As an active quantum computing enthusiast who likes to participate in cutting edge quantum events, I had a chance to review the book and found the content super exciting with breadth and depth of interesting topics. It is comprehensive in the sense that it reviews the basic ingredients of linear algebra and quantum mechanics that are necessary for the current applications realized through quantum machine learning among other fields of inquiry. I am excited use the book as a reference that puts together valuable quantum computing information at a time when it is at its early stages of usefulness. The proofs are concise, important themes are discussed, and the range of applications is rewarding. The algorithms discussed can be implemented through one's favorite quantum hardware.

Amazon Verified review

Sadman Dec 22, 2022

I recently read the book "Quantum Machine Learning and Optimization in Finance" and I was thoroughly impressed. The authors did a fantastic job of explaining complex principles from Quantum Information Sciences, Computer Science, and Optimization Theory in a clear and concise manner.One of the things I appreciated most about the book was the way it seamlessly blended these different disciplines together to provide a comprehensive overview of quantum machine learning and optimization in finance. The authors clearly have a deep understanding of each subject and are able to explain the concepts in a way that is accessible to readers who may not have a background in all of these areas.The book also includes numerous examples and case studies to illustrate the concepts being discussed, which helped me better understand how these principles can be applied in real-world situations.Overall, I highly recommend "Quantum Machine Learning and Optimization in Finance" to anyone interested in learning more about the intersection of quantum information sciences, computer science, and optimization theory as applied to finance. It is a valuable resource for professionals working in the finance industry, as well as researchers and students studying these topics.

Amazon Verified review

Jun Qi Dec 05, 2022

This book is a must-read one for the beginner to start the learning journey of quantum computing for machine learning, particularly in the application of finance. The first chapter concisely introduces the necessary foundations of quantum mechanics and important concepts of quantum computing. Then, the book reviews the adiabatic quantum computing protocol with optimization algorithms in finance. The gate model quantum computing is the core technique in this book, and the theoretical and application of quantum neural networks are comprehensively discussed in Part II.Overall, it is the best introductory book I have ever read for quantum machine learning and optimization algorithms in finance. Highly recommend!

Amazon Verified review

Joydeep Dec 11, 2022

Even though the title of this book says "Quantum Machine Learning and Optimisation in Finance", which initially seems intimidating, but I must say, the book starts with very basic questions like "Why Quantum Computing" & "Why Quantum Machine Learning" and then built the knowledge base from ground up.There are multiple focus areas of this book starting from 'practical and real-world applications of Quantum Machine Learning (QML)' to 'hybrid quantum-classical computational protocols' to current 'major QML algorithms' which has shown signs of potential quantum advantage. The implementation of those knowledge has been presented mostly on the hardware-agnostic way and focuses on the details of 'fundamental characteristics of the algorithms'.This book takes finance domain to showcase how QML can be applied to NP-hard problems and it's practical use cases in finance like 'portfolio optimisation, credit card default prediction, credit approvals, and generation of synthetic market data' etc. This books seems to cater a vast user-base, starting from beginner to researchers to the professionals in the finance domain and presented the content in a very lucid way.From content point of view the coverage is vast which comprises of 'Linear Algebra & Matrix decompositions, Adiabatic Quantum Computing, QUBO problem, Quantum Boosting, Quantum Boltzmann Machine, Parameterised Quantum Circuits (PQCs), Quantum Neural Network (QNN), Quantum Circuit Born Machine(QCBM), Variational Quantum Eigensolver (VQE), Quantum Approximate Optimisation Algorithm etc'.From the implementation point of view, I think, due to it's hardware-agnostic way of explanation, the book does not cover any code samples either through qiskit/Q#/Cirq or any other quantum programing language, but I am sure during next editions, the authors will consider this as well.Overall the way authors covered the depth and breadth of the knowledge in this book is highly praiseworthy.

Amazon Verified review

Siddhant Kochrekar Nov 26, 2022

The book came in pretty good condition, and I was waiting for this book to get published as I am taking a course in Applied Quantum Computing this semester. The reader is not expected to have any prior knowledge of Quantum mechanics, but the authors introduce the subject from the ground up.The authors have dedicated the first two chapters as a refresher in Quantum Computing. So the book gradually transits from mechanics to computing to applied Machine Learning and Finance. Although ML and Finance are the application domains in this book, you need to know the problem for which you are seeking QC solutions.In some parts, practical implications are lacking, but that is the shortcomings of this evolving field. But I recommend jumping in right now and catching the wave rather than waiting for the field and hardware to evolve fully. There are still a handful of executions shown on NISQ hardware. Power and practical extensions of analog quantum computers are succinctly displayed in this book. Certain blocks of text are assisted with pseudo-code, which makes it helpful while reading the material.They have compared QC with Machine Learning and Deep Learning algorithms in the form of clear analogies and explained in simple language suitable for all levels of readers. Quantum Boosting is my favorite part in this book as I have worked rigorously on XGBoost and other Boosting techniques in the Fintech industry. The book expands on ideas presented in research papers in the Quantum field and covers a few Finance case studies in Lending and Portfolio Management. Some advanced chapters have also tied QC with classical probabilistic approaches.On a high level, this book talks about all the topics from the angle of feasibility, error management, and resolution techniques. In the coming months, I would not be surprised if this text is used as supplementary reading material in applied cryptography courses in universities. I highly recommend this book to people studying Quantitative Finance.

Amazon Verified review

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

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