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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

Annealers and other devices

We have mostly talked about digital quantum computers, which are computers that use the abstraction of gates to operate on qubits. But quantum annealers such as those used in Chapters 5 to 7 (D-Wave’s quantum annealers) are also subject to errors and problems when dealing with larger-scale problems, mainly when increasing the number of assets involved in our operations.

If we take the example of portfolio optimization, D-Wave provides up to 5,000 qubit chips, which could potentially mean up to 5,000 asset portfolios having to be optimized.

Annealers require problems to be encoded or mapped onto their hardware, which involves representing the assets using the QUBO or Ising models and assigning them to specific qubits on their chips. Then, relationships between those variables are mapped to the couplings between qubits. Those links will carry the parameters associated with a given pair, which is often represented by J ij in the canonical...

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