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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
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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

Simulators and HPC’s Role in the NISQ Era

Now that we know how to make quantum and classical computing resources available and have reviewed how to pose our problems in both domains, we should evaluate the available mechanisms and strategies for exploiting those resources efficiently. By that, we mean cost and time efficiency, given that those axes will also need to be considered when it comes to including these techniques in our company’s daily processes.

Nowadays, the classical resources in most companies comprise a mixture of on-premises and cloud-enabled resources. This is the common case for most experimental projects aiming to improve operational processes using analytics. Ephemeral computing resources may have different needs, depending on the project or the nature of the technique we envision using. That is why the cloud-native pay-per-use model has become a good option for most companies.

Depending on the tasks, graphical processing units (GPUs) for machine...

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