Financial Modeling Using Quantum Computing E-Book

Anshul Saxena

• Herausgeber: Packt Publishing
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 399

Veröffentlichungsjahr: 2023

Beschreibung

Elevate your problem-solving prowess by using cutting-edge quantum machine learning algorithms in the financial domain

Purchase of the print or Kindle book includes a free PDF eBook

Key Features

Learn to solve financial analysis problems by harnessing quantum power

Unlock the benefits of quantum machine learning and its potential to solve problems

Train QML to solve portfolio optimization and risk analytics problems

Book Description

Quantum computing has the potential to revolutionize the computing paradigm. By integrating quantum algorithms with artificial intelligence and machine learning, we can harness the power of qubits to deliver comprehensive and optimized solutions for intricate financial problems.

This book offers step-by-step guidance on using various quantum algorithm frameworks within a Python environment, enabling you to tackle business challenges in finance. With the use of contrasting solutions from well-known Python libraries with quantum algorithms, you’ll discover the advantages of the quantum approach. Focusing on clarity, the authors expertly present complex quantum algorithms in a straightforward, yet comprehensive way. Throughout the book, you'll become adept at working with simple programs illustrating quantum computing principles. Gradually, you'll progress to more sophisticated programs and algorithms that harness the full power of quantum computing.

By the end of this book, you’ll be able to design, implement and run your own quantum computing programs to turbocharge your financial modelling.

What you will learn

Examine quantum computing frameworks, models, and techniques

Get to grips with QC's impact on financial modelling and simulations

Utilize Qiskit and Pennylane for financial analyses

Employ renowned NISQ algorithms in model building

Discover best practices for QML algorithm

Solve data mining issues with QML algorithms

Who this book is for

This book is for financial practitioners, quantitative analysts, or developers; looking to bring the power of quantum computing to their organizations. This is an essential resource written for finance professionals, who want to harness the power of quantum computers for solving real-world financial problems. A basic understanding of Python, calculus, linear algebra, and quantum computing is a prerequisite.

Leseprobe

Financial Modeling Using Quantum Computing

Design and manage quantum machine learning solutions for financial analysis and decision making

Anshul Saxena

Javier Mancilla

Iraitz Montalban

Christophe Pere

BIRMINGHAM—MUMBAI

Financial Modeling Using Quantum Computing

Copyright © 2023 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author(s), nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Group Product Manager: Reshma Raman

Publishing Product Manager: Apeksha Shetty

Senior Editor: Sushma Reddy

Technical Editor: Kavyashree K S

Copy Editor: Safis Editing

Book Project Manager: Kirti Pisat

Project Coordinator: Farheen Fathima

Proofreader: Safis Editing

Indexer: Manju Arasan

Production Designer: Prashant Ghare

Marketing Coordinator: Nivedita Singh

First published: May 2023

Production reference: 1300523

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham

B3 2PB, UK.

ISBN 978-1-80461-842-4

www.packtpub.com

To my mother, Jeannette, and the memory of my father, Patricio, for teaching me the power of perseverance. To my beloved wife, Sandy, and my sons, Agustín, Francisco, and Máximo, for being the light of my life and a constant inspiration.

– Javier Mancilla

To my family, pets included.

– Iraitz Montalban

To my wife and my sons, who bring light and happiness to my world.

– Christophe Pere, Ph.D.

To my loving wife, Jyoti, who inspires me to do better every day, and to Dr. Richard Feynman, for everything quantum.

– Dr. Anshul Saxena

Contributors

About the authors

Dr. Anshul Saxena holds a Ph.D. in applied AI and business analytics. He has over 13 years of work experience across IT companies including TCS and Northern Trust in various business analytics and decision sciences roles. He has completed certification courses in Joint AI and Quantum Expert from IISC and Quantum Computing for Managers (BIMTECH). He is a SAS-certified predictive modeler and has undergone several T3 training courses arranged by IBM under its university connect program. He has also handled corporate training for companies such as IBM and TCS. Currently, he is working as a professor and consultant for various financial organizations. His area of interest includes risk analytics and the application of quantum physics in stock markets.

Javier Mancilla is a Ph.D. candidate in quantum computing and holds a master’s degree in data management and innovation. He has more than 15 years of experience in digital transformation projects, with the last 8 years mostly dedicated to artificial intelligence, machine learning, and quantum computing, with more than 35 projects executed around these technologies. He has more than 8 certifications in quantum computing matters from institutions including MIT xPro, KAIST, IBM, Saint Petersburg University, and BIMTECH. He was also selected as one of the Top 20 Quantum Computing Linkedin Voices by Barcelonaqbit (a quantum organization in Spain) for both 2022 and 2023. Currently, he holds the role of quantum machine learning advisor and researcher for different companies and organizations in the finance industry in Europe and Latin America. Recently, he has been exploring the quantum gaming ecosystem and how to democratize quantum literacy.

Iraitz Montalban is a Ph.D. candidate at the University of the Basque Country and holds master’s degrees in mathematical modeling from the same institution, in data protection from the University of la Rioja, and in quantum technologies from the Polytechnic University of Madrid. He holds the Qiskit Developer Certificate as well as other relevant certifications around agile frameworks and innovation adoption strategies for large organizations. He has spent more than 15 years working with data analytics and digital transformation, 7 of which were dedicated to AI and ML adoption by large organizations. As an assistant professor in several universities, he has contributed to creating big data and analytics curriculums as well as teaching on these topics.

Christophe Pere is an applied quantum machine learning researcher and lead scientist originally from Paris, France. He has a Ph.D. in astrophysics from Université Côte d’Azur. After his Ph.D., he left the academic world for a career in artificial intelligence as an applied industry researcher. He learned quantum computing during his Ph.D. in his free time, starting as a passion and becoming his new career. He actively democratizes quantum computing to help other people and companies enter this new field.

Additional contributor

Shadab Hussain works as a developer advocate (for data science and machine learning) at the London Stock Exchange Group and co-founded Quantum Computing India. He formerly worked as a data scientist for multiple startups and MNCs. He has around 5 years of experience in ML, analytics, and building end-to-end ML pipelines on the cloud using the agile framework, and has also gained expertise in quantum computing from institutions such as TheCodingSchool and IBM. He has also published a few research papers at national and international conferences and is presently investigating applications of quantum computing and machine learning in finance and healthcare.

About the reviewers

Santanu Ganguly has worked in quantum computing, cloud computing, data networking, and security for more than 23 years. He has worked in Switzerland and is now based in the United Kingdom, where he has held senior-level positions at various Silicon Valley vendors. He presently works for a quantum computing startup leading global projects related to quantum communication and machine learning, among other technologies. He has two postgraduate degrees in mathematics and observational astrophysics and has research experience, patents, and publications in quantum computing, silicon photonics, and laser spectroscopy. He is the author of the 2021 Apress publication, Quantum Machine Learning: An Applied Approach, and a reviewer of other books on the subject.

Jonathan Hardy has over 25 years of experience in financial services in areas such as lending, treasury, software development, management consulting, and project management, and is considered an industry-wide expert in intelligent automation and digital transformation. He holds an undergraduate degree from the University of Alabama in corporate finance, and an MBA in finance from Mercer University. He holds designations as a Financial Risk Manager (FRM) and a Project Management Professional (PMP) from the Global Association of Risk Professionals and the Project Management Institute respectively, along with credentials in agile project management and artificial intelligence as well. He was a founding member of an Intelligent Automation (IA) CoE at one major Fortune 500 company, while providing thought leadership in the startup efforts of another. He has been married for 20 years and is the father of two wonderful children. Please feel free to reach out on LinkedIn if you want to connect.

Alex Khan is an advisor, entrepreneur, and educator in quantum computing. He is the CEO of ZebraKet, a Canadian startup in supply chain optimization using quantum methods. He has had roles at Chicago Quantum, where he co-authored papers on portfolio optimization using D-Wave, at Harrisburg University where he taught quantum computing, and at QuantFi. He continues to be an advisor at QuSecure.

Alex is an experienced health IT executive. He has a BSME from Purdue University, an MSME from KSU, an MBA from Duke University, and a certificate in quantum computing from MITxPro. He is the author of Quantum Computing Experimentation with Amazon Braket and has been recognized by The Quantum Daily as one of the 126 advisors shaping the quantum computing industry.

Table of Contents

Preface

Part 1: Basic Applications of Quantum Computing in Finance

1

Quantum Computing Paradigm

The evolution of quantum technology and its related paradigms

The evolution of computing paradigms

Business challenges and technology solutions

Current business challenges and limitations of digital technology

Basic quantum mechanics principles and their application

The emerging role of quantum computing technology for next-generation businesses

From quantum mechanics to quantum computing

Approaches to quantum innovation

Quantum computing value chain

The business application of quantum computing

Global players in the quantum computing domain across the value chain

Building a quantum computing strategy implementation roadmap

Building a workforce for a quantum leap

Summary

2

Quantum Machine Learning Algorithms and Their Ecosystem

Technical requirements

Foundational quantum algorithms

Deutsch-Jozsa algorithm

Grover’s algorithm

Shor’s algorithm

QML algorithms

Variational Quantum Classifiers

Quantum neural networks

Quantum Support Vector Classification (QSVC)

Variational Quantum Eigensolver

QAOA

Quantum programming

Qiskit

PennyLane

Cirq

Quantum Development Kit (QDK)

Quantum clouds

IBM Quantum

Amazon Braket

Microsoft Quantum

Summary

References

3

Quantum Finance Landscape

Introduction to types of financial institutions

Retail banks

Investment banks

Investment managers

Government institutions

Exchanges/clearing houses

Payment processors

Insurance providers

Key problems in financial services

Asset management

Risk analysis

Investment and portfolios

Profiling and data-driven services

Customer identification and customer retention

Information gap

Customization

Fraud detection

Summary

Further reading

References

Part 2: Advanced Applications of Quantum Computing in Finance

4

Derivative Valuation

Derivatives pricing – the theoretical aspects

The time value of money

Case study one

Securities pricing

Case study two

Derivatives pricing

Case study three

Derivatives pricing – theory

The Black-Scholes-Merton (BSM) model

Computational models

Machine learning

Geometric Brownian motion

Quantum computing

Implementation in Qiskit

Using qGANs for price distribution loading

Summary

Further reading

References

5

Portfolio Management

Financial portfolio management

Financial portfolio diversification

Financial asset allocation

Financial risk tolerance

Financial portfolio optimization

MPT

The efficient frontier

Example

Case study

Financial portfolio simulation

Financial portfolio simulation techniques

Portfolio management using traditional machine learning algorithms

Classical implementation

Quantum algorithm portfolio management implementation

Quantum annealers

D-Wave implementation

Qiskit implementation

Conclusion

6

Credit Risk Analytics

The relevance of credit risk analysis

Data exploration and preparation to execute both ML and QML models

Features analysis

Data preprocessing

Real business data

Synthetic data

Case study

Provider of the data

Features

Implementation of classical and quantum machine learning algorithms for a credit scoring scenario

Data preparation

Preprocessing

Quantum Support Vector Machines

QNNs

VQC

Classification key performance indicators

Balanced accuracy, or ROC-AUC score

Conclusion

Further reading

7

Implementation in Quantum Clouds

Challenges of quantum implementations on cloud platforms

D-Wave

IBM Quantum

Amazon Braket

Azure

Cost estimation

Summary

Further reading

References

Part 3: Upcoming Quantum Scenario

8

Simulators and HPC’s Role in the NISQ Era

Local simulation of noise models

Tensor networks for simulation

GPUs

Summary

Further reading

References

9

NISQ Quantum Hardware Roadmap

Logical versus physical qubits

Fault-tolerant approaches

Circuit knitting

Error mitigation

Annealers and other devices

Summary

Further reading

References

10

Business Implementation

The quantum workforce barrier

Case study

Key skills for training resources

Infrastructure integration barrier

Case study

Identifying the potentiality of advantage with QML

Case study

Funding or budgeting issues

Case study

Market maturity, hype, and skepticism

Case study

Road map for early adoption of quantum computing for financial institutions

Case study

Quantum managers’ training

Case study

Conclusions

References

Index

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Part 1: Basic Applications of Quantum Computing in Finance

This segment addresses the complexities of digital technology, its challenges, and the role of quantum computing in overcoming these limitations. It specifically highlights quantum machine learning, a technique that leverages quantum bits and operations to boost computational speed and data storage. Readers will gain a theoretical and practical understanding of quantum machine learning algorithms. The chapter also transitions into quantum finance, shedding light on its potential and illustrating the types of financial issues that can be tackled using quantum computing principles.

This part has the following chapters:

Chapter 1, Quantum Computing ParadigmChapter 2, Quantum Machine Learning AlgorithmsChapter 3, Quantum Finance Landscape