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

Portfolio management using traditional machine learning algorithms

Classical implementation

Portfolio optimization is a problem related to the financial services and banking industry that emerged with Markovitz’s seminal paper in 1952 (https://onlinelibrary.wiley.com/doi/full/10.1111/j.1540-6261.1952.tb01525.x). The model describes a set of assets x i X from which a subset needs to be picked to maximize the revenue, while minimizing the risk at 𝑡 future time steps. For a given period, each asset has an expected return linked to it, and the covariance between assets sets the risk amount in terms of diversification (for the sake of simplicity). The idea behind this diversification is that if we only invest in the assets with the highest revenue, the risk of them being driven by the same factors if our investment fails is bigger than if we diversify our portfolio. We will focus on a single-time-step process, assuming that local optima are part of the longer...