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

Summary

In this chapter, we explored a complex use case to forecast the future, the future of how markets will evolve, how stock prices will evolve, and so on a non-trivial task indeed. We showed how classical statistics extensively used in the field can be used in a quantum regime. This brings some benefits but also, given its current status and the limitations of the devices themselves, poses other impediments we would need to work around.

Encoding a probability distribution on a gate-based quantum device entails some resolution loss. Probability distributions may need to be truncated, and value ranges will be rendered as the same discrete bin that the future price of an option may be placed. This limitation will probably change when bigger devices are made available so that larger distributions can be encoded. However, it is indeed a limitation we should have present in our minds when adopting these techniques.

Of course, there are benefits, as quantum devices are capable...