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 the challenges that working on real hardware may pose. Depending on the specific nature of the hardware, regardless of whether it is purpose-specific, as in the case of quantum annealers, or one of the many implementations of digital quantum computers, these concepts are still hard to omit.

Being aware that the mapping for a given problem is being done at a hardware level, paying attention to which qubits are used, their associated error, and how this will be reflected in the outcome, you can implement countermeasures so that the results still offer enough resolution. That way, the advantage that’s expected from quantum computation can still be significant.

By understanding the different challenges and how they may affect a given problem setup, you can choose the appropriate hardware that can better accommodate the problem.

Annealers can be used for large problems but not as large as you might think in terms of embedding restrictions...