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 saw that there are many ways to simulate a quantum computer before running it on an actual device. We saw that there are also some implications regarding the limited availability, errors, and specific characteristics of the real hardware to be considered and that classical computers are not yet done when it comes to quantum computing.

Establishing a strategy to validate our circuits, evaluate their potential, and decide where those algorithms will run requires understanding the set of options provided by almost all quantum companies.

Tensor networks provide a powerful mathematical framework to simulate complex systems efficiently. GPUs have also placed their bet. Even combining both has proven to be a valid approach for simulating large devices.

Distributed computation is anticipated to be the next hurdle to overcome, necessitating a certain level of technical expertise to harness its potential efficiently. Similar to the trajectory followed by...