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

Quantum computing

As we saw in the previous section, in order to estimate the future price of an equity, many iterations over potential future prices need to be run. However, what if we could establish a way to load those potential distributions into quantum states and evaluate them using quantum devices? The following subsection will dig into different ways to load those future price distributions into quantum states using existing solutions for direct loading, such as Qiskit functionalities and adversarial training using PennyLane, which might be better suited for ML tasks due to its differentiable programming approach (similar to TensorFlow or PyTorch in the classical ML domain).

Implementation in Qiskit

As discussed in Chapter 2, Qiskit is one of the most mature quantum computing frameworks available and counts with higher-level modules so that specific applications can be easily translated to the quantum regime. This is the case with Qiskit Finance, which we will explore...