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

Implementation of classical and quantum machine learning algorithms for a credit scoring scenario

Applying machine learning and quantum machine learning for credit scoring challenges requires the development of a prediction model that can properly determine an individual’s or company’s creditworthiness. Typically, this procedure, as shown in the steps described previously, includes data collection, data enrichment, data preparation, feature engineering, feature selection, model selection, model training, model evaluation, and subsequently, deployment. In this section, we will cover most of the previous concepts and procedures, assuming that the data is already encoded to numerical variables and the feature has been selected.

Data preparation

First, the data needs to be loaded. This data will come in one of the more well-known formats in the industry, which is CSV. The information that will load into the notebook, as previously detailed, is in a classical format...