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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

Further reading

For those interested in diving deeper into some of the techniques mentioned in this chapter, here are some recommendations that should help you understand the basics.

One of the most interesting and challenging frameworks we have discussed is tensor networks. Many resources can be found in the literature. Still, two that we can recommend are the work by Biamonte and Bergholm from 2017, which provides a solid foundation to understand its potential better. For those more hands-on engineers, the Quimb (Gray, 2018) and Jet (Vincent et al., 2022) Python packages provide a fun way to learn and experiment.

Similarly, distributed computation has a path, and works by Zaharia et al. (2010) on Apache Spark and Moritz et al. (2018) on Ray are leading the path toward easy-to-implement distributed solutions.

Something particularly interesting is the contribution of the Baidu team to the existing PaddlePaddle framework (Ma et al., 2020). Not only have they provided an industrial...

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