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

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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!

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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Frequently bought together

Quantum Machine Learning and Optimisation in Finance

$59.99

Modern Time Series Forecasting with Python

$52.99

Essential Mathematics for Quantum Computing

$29.99

Total $ 142.97

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James L. Weaver Dec 01, 2022

This book covers a lot of ground. After covering the necessary aspects of linear algebra and trig require for quantum mechanics, as well as postulates of quantum mechanics, it dives into quantum computing topics. Some topics are covered in the context of adiabatic quantum computing, and some in the context of gate model quantum computing. I really appreciate how the book takes the practical approach of explaining how optimization, machine learning, and financial modeling may be achieved on quantum computing hardware available in the near term.

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.

Miles Price Nov 29, 2022

This book offers an insightful and entertaining look at how to apply Quantum Computing to Finance. It covers topics such as Quantum Mechanics, Quantum Electro Chromodynamics, Applied Quantum Computing, and Quantum Field Theory in the context of financial engineering. Additionally, it discusses tools such as Neural Networks, Fuzzy Logics and Chaos Theory as they relate to Quantitative Finance. This makes it a must-read for any avid quant!As a fintech and EmTech pro with a background in DeepTech, the basic concept of Quantum Computing fueling the field of Finance is easy to understand, and the framework provided is perfect for when I build quantum computing-based trade algos (primarily when utilizing the fundamentals of quantum computing based ML / QuantumML. I especially enjoyed introducing the advancement of civilization and how well every leap in developing technological progress (in terms of topics discussed throughout the book) was laid out to emphasize the passage of time in regards to how quantum computing applied to AI in Finance is the quintessential next step.Lastly, this book provides a straightforward approach to understanding various disciplines, which includes: deep learning-based neural networks, ML foundations (and how they are computed within seconds to minutes rather than hours, days, and weeks), an overview of ML algorithms, and how much of quantum ML (coded in Q# or evolved from conventional algorithms into ML algos running on Qubits) is heavily and a perfect application for the field of Finance. Speed is everything, and QuantumML delivers.

Steven Fernandes Nov 01, 2022

The title of the book can be misleading. The book is not about understanding quantum machine learning algorithms. Instead, it focuses on mathematical aspects of quantum computing written by a physicist. The book's first part emphasises presenting five postulates of quantum mechanics, including quantum annealing. The book does not provide any practical implementation. It presents a problem; for example, the Credit card defaults using credit card clients (DCCC) dataset is available from the UCI Machine Learning. However, without providing the code, the author presents the classification results, such as confusion metric with accuracy, precision and recall values. The second part of the book focuses on classical and quantum logic gates. There is no implementation of the quantum gates using Qiskit, Braket or Q#. The quantum neural networks were quite disappointing without helping the reader to understand the practical implementation steps of quantum neural networks and only providing mathematical aspects of quantum generative adversarial networks. The author of this book is PhD. in Mathematics. The book is suitable for readers who want to learn the mathematical aspects of quantum mechanics.

PPG Feb 01, 2023

I liked the level of mathematical detail. It has enough details but at the same is not overly complicated as the authors have focused only on key mathematical concepts. I liked their treatment of condition number, an important concept that many people don’t cover in usual physics textbooks. I felt that the book assumed, however, that most readers know the finance material in and out. It would be good to include some quick crash course chapter on finance concepts so that this book reaches a wider audience.

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

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