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Financial Modeling Using Quantum Computing

You're reading from  Financial Modeling Using Quantum Computing

Product type Book
Published in May 2023
Publisher Packt
ISBN-13 9781804618424
Pages 292 pages
Edition 1st Edition
Languages
Authors (4):
Anshul Saxena Anshul Saxena
Profile icon Anshul Saxena
Javier Mancilla Javier Mancilla
Profile icon Javier Mancilla
Iraitz Montalban Iraitz Montalban
Profile icon Iraitz Montalban
Christophe Pere Christophe Pere
Profile icon Christophe Pere
View More author details

Table of Contents (16) Chapters

Preface 1. Part 1: Basic Applications of Quantum Computing in Finance
2. Chapter 1: Quantum Computing Paradigm 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

Road map for early adoption of quantum computing for financial institutions

The financial institution might use a cloud-based quantum simulator to model and test quantum algorithms for risk analysis and portfolio optimization tasks. This allows the institution to evaluate the potential benefits of quantum computing without investing in its quantum hardware.

Once the institution has found a promising quantum algorithm, it can test and improve it using a cloud-based quantum simulator. This can involve running simulations on different datasets and tweaking the algorithm to make it work better and more accurately.

Once the institution is satisfied with the performance of the quantum algorithm, it can use quantum hardware to run the algorithm on real data. The institution can then use the results of the quantum computation to help them make decisions or improve their risk analysis, portfolio optimization, or other financial operations.

Case study

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