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 algorithm portfolio management implementation

Quantum annealers

Quantum annealers are specialized machines capable of finding the minimum energy solution to a given problem, following the adiabatic principle. We talked about some of these machines in Chapter 2, but we will now cover in detail how they can be used to solve a problem such as portfolio optimization.

Quantum annealers require a target problem, set in its matrix form, to place variables as a mask. In our portfolio example, solutions will be encoded as binary decisions if the asset n will be included in our final portfolio. Therefore, our problem matrix should reflect the effect of including an asset or not in a solution.

For this, often in the literature, it is found that problems need to be placed on their QUBO (or Ising) form. QUBO stands for Quadratic Unconstrained Binary Optimization, which means binary variables are considered (0 or 1), only two-way multiplications are represented (X i ×...