Book Image

Financial Modeling Using Quantum Computing

By : Anshul Saxena, Javier Mancilla, Iraitz Montalban, Christophe Pere
5 (1)
Book Image

Financial Modeling Using Quantum Computing

5 (1)
By: Anshul Saxena, Javier Mancilla, Iraitz Montalban, Christophe Pere

Overview of this book

Quantum computing has the potential to revolutionize the computing paradigm. By integrating quantum algorithms with artificial intelligence and machine learning, we can harness the power of qubits to deliver comprehensive and optimized solutions for intricate financial problems. This book offers step-by-step guidance on using various quantum algorithm frameworks within a Python environment, enabling you to tackle business challenges in finance. With the use of contrasting solutions from well-known Python libraries with quantum algorithms, you’ll discover the advantages of the quantum approach. Focusing on clarity, the authors expertly present complex quantum algorithms in a straightforward, yet comprehensive way. Throughout the book, you'll become adept at working with simple programs illustrating quantum computing principles. Gradually, you'll progress to more sophisticated programs and algorithms that harness the full power of quantum computing. By the end of this book, you’ll be able to design, implement and run your own quantum computing programs to turbocharge your financial modelling.
Table of Contents (16 chapters)
1
Part 1: Basic Applications of Quantum Computing in Finance
5
Part 2: Advanced Applications of Quantum Computing in Finance
10
Part 3: Upcoming Quantum Scenario

Data exploration and preparation to execute both ML and QML models

As mentioned before, in this chapter, we will walk you through the implementation of hybrid quantum-classical algorithms and how they behave in a real-world scenario in finance, but before you start playing with them in a professional setup, you should think – or at least review – some the following concepts.

Data enrichment refers to the process of enriching or supplementing an existing dataset with extra information. Data enrichment in the context of credit scoring systems is the use of additional data sources to supplement extra variables and features that could come from a credit bureau or a non-traditional source (e.g., mobile data mining) in order to increase the accuracy of credit risk assessments.

By incorporating additional data sources like public records (digital footprints), social media behavior, financial history, open finance, and other alternative data sources, data enrichment can...