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Financial Modeling Using Quantum Computing

You're reading from   Financial Modeling Using Quantum Computing Design and manage quantum machine learning solutions for financial analysis and decision making

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Product type Paperback
Published in May 2023
Publisher Packt
ISBN-13 9781804618424
Length 292 pages
Edition 1st Edition
Languages
Concepts
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Authors (4):
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Iraitz Montalban Iraitz Montalban
Author Profile Icon Iraitz Montalban
Iraitz Montalban
Anshul Saxena Anshul Saxena
Author Profile Icon Anshul Saxena
Anshul Saxena
Javier Mancilla Javier Mancilla
Author Profile Icon Javier Mancilla
Javier Mancilla
Christophe Pere Christophe Pere
Author Profile Icon Christophe Pere
Christophe Pere
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Toc

Table of Contents (16) Chapters Close

Preface 1. Part 1: Basic Applications of Quantum Computing in Finance
2. Chapter 1: Quantum Computing Paradigm FREE CHAPTER 3. Chapter 2: Quantum Machine Learning Algorithms and Their Ecosystem 4. Chapter 3: Quantum Finance Landscape 5. Part 2: Advanced Applications of Quantum Computing in Finance
6. Chapter 4: Derivative Valuation 7. Chapter 5: Portfolio Management 8. Chapter 6: Credit Risk Analytics 9. Chapter 7: Implementation in Quantum Clouds 10. Part 3: Upcoming Quantum Scenario
11. Chapter 8: Simulators and HPC’s Role in the NISQ Era 12. Chapter 9: NISQ Quantum Hardware Roadmap 13. Chapter 10: Business Implementation 14. Index 15. Other Books You May Enjoy

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

Applying machine learning and quantum machine learning for credit scoring challenges requires the development of a prediction model that can properly determine an individual’s or company’s creditworthiness. Typically, this procedure, as shown in the steps described previously, includes data collection, data enrichment, data preparation, feature engineering, feature selection, model selection, model training, model evaluation, and subsequently, deployment. In this section, we will cover most of the previous concepts and procedures, assuming that the data is already encoded to numerical variables and the feature has been selected.

Data preparation

First, the data needs to be loaded. This data will come in one of the more well-known formats in the industry, which is CSV. The information that will load into the notebook, as previously detailed, is in a classical format...

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