Financial Modeling Using
Quantum Computing

✍ Scribed by Anshul Saxena, Javier Mancilla, Iraitz Montalban and Christophe Pere

• Publisher: Packt Publishing Pvt Ltd
• Year: 2023
• Tongue: English
• Leaves: 292
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Financial Modeling Using Quantum Computing
Contributors
About the authors
Additional contributor
About the reviewers
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
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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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