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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

Product type Paperback

Published in May 2023

Publisher Packt

ISBN-13 9781804618424

Length 292 pages

Edition 1st Edition

Languages

Python

Tools

Pennylane

Concepts

Design

Authors (4):

Iraitz Montalban

Anshul Saxena

Javier Mancilla

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 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

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15. Other Books You May Enjoy

Error mitigation

Some common sources of error can be more systematically tackled since measuring the classical outcome of quantum hardware is not free of errors. Luckily, this type of error can be tackled by observing the common errors that are made upon readout and compensating for post-processing the outcome.

If we look into our IBM Quantum Experience service once more, we could request the readout error for a given device. In Figure 9.6, we can observe how any operation that’s done on qubits 10 and 15, upon measurement, could be misinterpreted:

Figure 9.6 – Readout error on IBM’s Toronto device (27 superconducting qubits Falcon r4)

Figure 9.6 – Readout error on IBM’s Toronto device (27 superconducting qubits Falcon r4)

These statistics can be derived by the simple act of placing an operation whose outcome is known (for example, X|ψ⟩) and recording the discrepancies upon measuring it for a significant number of tryouts. If those statistics are known, you can compensate for the measurements that are obtained...

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Authors (4)

Anshul Saxena

Anshul Saxena

Professor Anshul Saxena is a quantum finance instructor at Christ University. His current research focus is abouton discovering the role of quantum computing in solving complex financial problems. He has filed three Indian patents and holds an international patent. He has authored a popular book on HR Analytics and has developed an automated Ppython library "Cognito" for data preprocessing. He has over a decade of work experience spreading across IT and financial services companies like TCS and Northern Trust in various business analytics and decision sciences roles. He has worked as a consultant and trainer with IBM ICE group and has trained more than 500 faculties pan India. Mr. Saxena has also worked as a Corporate Trainer and has conducted training on data science for more than 600 IT employees. He is a SAS certified predictive modeler and has recently completed a certificate in "Quantum computing for managers" for BIMTECH. He holds an MBA degree in Finance from IBS Bangalore and is pursuing his Ph.D. in Financial Risk Analytics

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Christophe Pere

Christophe Pere

Christophe Pere is an Applied Quantum Machine Learning Researcher and Lead Scientist originally from Paris, France. He has a Ph.D. in Astrophysics from Université Côte d'Azur. After his Ph.D., he left the academic world for a career in Artificial Intelligence as an Applied Industry Researcher. He learned quantum computing during his Ph.D. in his free time, starting as a passion and becoming his new career. He actively democratizes Quantum Computing to help people and companies enter this new field.

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Iraitz Montalban

Iraitz Montalban

Iraitz Montalban is currently Quantum Software Engineer for Kipu Quantum GmbH and PhD candidate at the University of the Basque Country in Quantum Machine Learning. He holds several master's degrees in Mathematical modelling, Data Protection and Quantum Technologies as well. Has hold positions of responsability in large organizations as well as coordinated Innovation practices in all of then given his trajectory as a reseacrher in AI and ML disciplines and his more than 15 years of experience in this field. He activelly collaborates with different universities and education institutions designing the curriculum and teaching in programs around BigData and Advanced Analytics

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Javier Mancilla

Javier Mancilla

Javier Mancilla is a Senior Data Scientist, and a Quantum Business and Programming Consultant. He is a Ph.D. candidate and Master in Data Management and Innovation. He has more than 15 years of experience in digital transformation projects, withand in the last 8 years mostly dedicated to artificial intelligence, machine learning, and quantum computing, with more than 35 projects executed around these technologies. He has more than 8 certifications in quantum computing matters from institutions like MIT xPro, KAIST, IBM, Saint Petersburg University, and BIMTECH. He also was selected as one of the Top 20 Quantum Computing Linkedin Voices by Barcelonaqbit (quantum organization in Spain). Currently, he has the role of quantum machine learning advisor for different companies and organizations in Europe and Latin America and is also an I + D + i (Investigation, Development, and Innovation) evaluator for different governments in LATAM such as Chile and Paraguay

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