This is the code repository for Financial Modeling using Quantum Computing, published by Packt.
Design and manage quantum machine learning solutions for financial analysis and decision making
This book covers the following exciting features: Examine quantum computing frameworks, models, and techniques Get to grips with QC's impact on financial modelling and simulations Utilize Qiskit and Pennylane for financial analyses Employ renowned NISQ algorithms in model building Discover best practices for QML algorithm Solve data mining issues with QML algorithms
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter04.
The code will look like the following:
import numpy as np
from scipy.stats import norm
t = 1.0 # year
K = 105 # Strike price
r = 0.05 # Riskless short rate
sigma = 0.25 # Volatility (stdev)
S0 = 100 # Present price
- Page 160 (Chapter 6): Balanced Accuracy, or ROC-AUC score should be Balanced Accuracy, and ROC-AUC score
- Page 161 (Chapter 6): AUC being equivalent to Balanced Accuracy is a simplification over binary outcome which is not the case when using probabilistic outcome (
.predict_proba()) for classification.
Following is what you need for this book: This book is for financial practitioners, quantitative analysts, or developers; looking to bring the power of quantum computing to their organizations. This is an essential resource written for finance professionals, who want to harness the power of quantum computers for solving real-world financial problems. A basic understanding of Python, calculus, linear algebra, and quantum computing is a prerequisite.
With the following software and hardware list you can run all code files present in the book (Chapter 4-9).
| Chapter | Software required | OS required |
|---|---|---|
| 4-9 | Python | Windows, Mac OS X, and Linux |
| 4-9 | Jupyter notebook | Windows, Mac OS X, and Linux |
| 7-9 | Dwave Leap | Windows, Mac OS X, and Linux |
| 7-9 | AWS Braket | Windows, Mac OS X, and Linux |
| 7-9 | Azure | Windows, Mac OS X, and Linux |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Dr. Anshul Saxena holds a Ph.D. in applied AI and business analytics. He has over 13 years of work experience across IT companies including TCS and Northern Trust in various business analytics and decision sciences roles. He has completed certification courses in Joint AI and Quantum Expert from IISC and Quantum Computing for Managers (BIMTECH). He is a SAS-certified predictive modeler and has undergone several T3 training courses arranged by IBM under its university connect program. He has also handled corporate training for companies such as IBM and TCS. Currently, he is working as a professor and consultant for various financial organizations. His area of interest includes risk analytics and the application of quantum physics in stock markets.
Javier Mancilla is a Ph.D. candidate in quantum computing and holds a master’s degree in data management and innovation. He has more than 15 years of experience in digital transformation projects, with 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 including MIT xPro, KAIST, IBM, Saint Petersburg University, and BIMTECH. He was also selected as one of the Top 20 Quantum Computing Linkedin Voices by Barcelonaqbit (a quantum organization in Spain) for both 2022 and 2023. Currently, he holds the role of quantum machine learning advisor and researcher for different companies and organizations in the finance industry in Europe and Latin America. Recently, he has been exploring the quantum gaming ecosystem and how to democratize quantum literacy.
Iraitz Montalban is a Ph.D. candidate at the University of the Basque Country and holds master’s degrees in mathematical modeling from the same institution, in data protection from the University of la Rioja, and in quantum technologies from the Polytechnic University of Madrid. He holds the Qiskit Developer Certificate as well as other relevant certifications around agile frameworks and innovation adoption strategies for large organizations. He has spent more than 15 years working with data analytics and digital transformation, 7 of which were dedicated to AI and ML adoption by large organizations. As an assistant professor in several universities, he has contributed to creating big data and analytics curriculums as well as teaching on these topics.
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 other people and companies enter this new field.