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

Cost estimation

We briefly mentioned resource estimation, but we would like to highlight the importance of this ability when aiming for a sustainable adoption strategy.

Quantum computing is at an early stage, requiring continuous and complex maintenance tasks to provide the best possible service. This is something that classical computing resources have mastered for a long time. Due to the limited ecosystem of hardware providers and the status of the technology, specifically, when aiming for real hardware, we will see costs ramp up significantly, even for the simplest providers. That is why resource estimation is such a crucial step in any QC pipeline, particularly if the model’s training requires iterations:

Figure 7.26 – Cost for Quantinuum on Azure (below) and IonQ in AWS (above)

Figure 7.26 – Cost for Quantinuum on Azure (below) and IonQ in AWS (above)

As an example, we could take the European Call Pricing from Chapter 4 and extract its underlying quantum circuit, which we already know is composed of a block...