Bill Wisotsky Jul 19, 2023

This book was organized in a very logical way taking you through the current financial quantum landscape. The book moves through creating a foundation quantum computing, the various technologies, and the evolution. It then spends time on a high level, discussing some important types of quantum machine learning algorithms, what they are and why they are used. The authors discuss different types of quantum programming and quantum cloud providers with examples. The authors talk about the financial landscape, what kinds of verticals exist, and the problems that could be potenitally addressed by quantum computing. Part 2 deep dives into these financial ML/QML problems and explains what they are, why they are important and how quantum could help. For example, the authors discuss in detail what portfolio optimization is and gives examples on how to solve it classically. They then discuss how it could benefit from using quantum and then give one or two examples of how to solve it using quantum. What is great is that multiple technologies are shown for the sample problem, (e.g. D-Wave, Qiskit, Circ, PennyLane, etc.). The authors then spend time talking in detail about quantum clouds and QaaS, with detailed explanations and screenshots from different quantum cloud providers. Finally, there is talk about the importance of simulators, noise and the possible future directions of quantum.All-in-all this book is a great addition to anyone needing to understand applied QML in finance and how various technologies and vendors fit into the field and how they could be used to for QML. The book did not have overcomplicated math and went comfortably deep enough that you could try things on your own, but don't need a PhD in physics or mathematics.Highly recommend.

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ML Enthusiast Jun 19, 2023

The book "Financial Modeling using Quantum Computing" is not an introduction to Quantum Computing. There are alreadymany of those on the market - so there is not necessarily a need for another one. It is a book about APPLICATIONS ofQuantum Computing in financial modeling and decision making. And there is no good presentation about this yet- so it is the right book at the right time!In this book, no mathematical or physical basics are taught in the first part; instead, the followingquestions are answered: What are the principles of Quantum Computing? What is the idea behind the most important algorithms?Which tools/frameworks/solutions can be used for Quantum Machine Learning in particular?How does Quantum Computing fit into the finance industry landscape?The second part of the book goes into more depth: How can the valuation of derivatives be implemented with Quantum Computing?How portfolio management ? And how can credit risk analysis (i.e. rating) be realized with Quantum Computing and what are theadvantages arise?Finally, the third part is to look at the synthesis of simulators, NISQ and HPC; the NISQ hardware roadmap and the implicationsfor potential applications.The book is particularly suitable for those who have a basic knowledge of the basics of Quantum Computing and are looking forits placement in a larger, application-related context. For those, the book is absolutely recommendable!

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Tiny Jul 05, 2023

Fully implementing the potential for quantum computing as a game-changer and not just another good idea fairy means comprehending how an industry sector can benefit. “Financial Modeling Using Quantum Computing” (Packt, 2023) by Anshul Saxena, PhD, Javier Mancilla Montero, Iraitz Montalban, and Christophe Pere, PhD does exactly that in painstakingly describing which quantum practices can best be translated into a significant ROI (Return on Investment) within the financial sector. The book provides coding examples for several popular quantum languages and describes where, and how, to implement those tools for business. Divided into three sections, the quantum computing paradigm, applications of quantum computing, and upcoming quantum scenarios, this work provides a practical guide to advocate and implement solutions for the business minded. Best recommended for those with a working knowledge of quantum computing and an interest in understanding financial implementations. The Quantum Computing Paradigm starts in the same place as all good quantum books, with a history of quantum computing and what makes it so different than classical methods. Reviews are provided for the most common existing algorithms, available toolsets, and the providers one might use to implement those tools. The last part then provides an overview of financial markets. This covers the various types of financial institutions. One of the most helpful parts was lasting those factors necessary to those institutions in considering new technology - asset management, risk analysis, Investment, profiling, customer identity and retention, Information gaps, customization, and fraud detection. Each area gains substantial benefits from quantum’s ability to handle multiple variables at a greater speed than classical optimization solutions. The middle portion examines derivative valuation, portfolio management and credit risk analytics. Many of these traditional approaches are based on a stochastic analysis, limited only by computing power. Descriptions appear for the Black-Scholes, Monte Carlo, and binomial methods currently in use by financial institutions. Examples with Qiskit and PennyLane provide samples to run one’s own code and examine the differences. Derivatives consider understanding future asset value, portfolio management describes including multiple sectors within those derivatives, and credit risk the potential for whether a bank should risk lending money. Each option can be maximized by understanding a wider variable range which the authors point out best occurs within the quantum annealing sector. Quantum annealing focuses specifically on optimization problems. While the existing coding samples focus on smaller problems, they are easily expandable to handle a real-world implementation. The last chapter here addresses the practical application of using quantum devices attached to a cloud rather than purchasing and maintaining a super-cooled laboratory for one’s own business. The last element considers some practical elements. A helpful definition occurs here in that a simulator is a classical computer running quantum solutions, an emulator introduces the errors when classical code transfers to a quantum implementation, and a device involves an actual solution such as Amazon Braket. The differences allow one to understand the importance of error mitigation within the quantum sphere. One central challenge emerges in the no-cloning theorem when the quantum process cannot be copied without collapsing the quantum state. The other challenge is the relation between physical qubits and logical qubits. Currently, the best solution shows that to receive one logical, error-corrected qubit requires at least 9 physical qubits. This adds perspective when considering the record for the largest quantum computing system is 433 bits, equating to about 40ish logical qubits. Though the book provided exceptional detail, further emphasis on the case studies would have been helpful The coding samples were excellent but the comparison to actual implementation only appeared in 1-2 paragraph segments. Understood that this is a theoretical overview rather than full practical implementation but knowing the existing marketplace solutions can be extremely beneficial. Another useful add would have been a side by side comparison of the different quantum cloud options. Descriptions appear for D-Wave, AWS, IBM, and Azure but a graphic showing the comparative capabilities would have aided greatly. Overall, “Financial Modeling Using Quantum Computing” was an excellent, focused work conveying exactly what the title says. If one is interested in quantum computing in general, it remains useful to understand the various applications. On the other hand, if one works in financial transactions daily, or is charged with improving future applications, this provides an excellent gateway to begin using those tools for oneself. Recommend for financial software developers and those with an interest in quantum computing.

Amazon Verified review

Aadi Sep 04, 2023

I liked the connection/flow of the book. It took me 3 months to read this book, was kind of unable to dedicate a lot of time, but it was easy to pick up from the checkpoint. The modelling was necessary for Quantum computing learning newer kids. I learnt some specifics of finances through this book too. If you are in financial domain, give it a try, its a new realm of possibility.

Amazon Verified review

Ram Rahul Pennam Jun 28, 2023

# Overview of this book:This is very interesting book I would say. To begin with, this book contains Part 1 which addresses “Basic Applications of Quantum Computing in Finance” which gives us basic understanding of what is quantum computing, evolution, principles and explains about some of the important algorithms such as Shor and Grover. It also highlights some of the key. concepts and explanation along with code samples for Python library Qiskit for Quantum programming. This part also introduces financial concepts is very helpful for people from different domains.---# Advanced concepts:The Part 2 of this book contains concepts related to advanced applications of QuantumComputing in Finance such as (Calculus) derivatives and its theoretical aspects. All these concepts are coined together in this and are related to QC. This section also includes Machine Learning, Case Studies which are very helpful for Analysts, financial concepts like Portfolio Management and Credit Risk Analytics along with QC can be implementations.This sections gives us insights about how companies like D-wave, IBM, Amazon and Microsoft implements QC in the cloud. Not sure but curious why this did not include Google’s QC.---# Future Proofing and Case StudiesThis Part 3 Section contains one of main technical stuff like simulation of noice models, error correction, assigning logical and physical circuits. Yes, design circuits is one of most important aspects in QC. This part is illustrated with circuit diagrams, graphs etc. as it’s very important to understand QC. Finally, this section has plenty of case studies which in my opinion are very helpful for understanding, updating with current market trends and also helps us to prepare for interviews.

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Financial Modeling Using Quantum Computing: Design and manage quantum machine learning solutions for financial analysis and decision making

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