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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

Infrastructure integration barrier

One of the natural barriers for companies looking to explore solutions with quantum computing is how to integrate them into their current operations. Depending on the case, the technology of real quantum hardware can be more or less prepared for real-time response and coexist with the current systems that the companies have deployed in the cloud. Particularly in the QML field, for classification challenges (credit scoring or fraud prediction), the instant response from a QC could be an issue to solve, since most of the machines have a queue system due to the small number of computers available. As a valid option, companies can use several types of simulators in the cloud to operate in a low range of qubits (most of the hybrid quantum-classical algorithms for QML operate quite well with a few tens of qubits) below the 40-qubit line.

The use of simulators can represent a good cost-efficient option, since the quantum algorithms can run faster (in...

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