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

Road map for early adoption of quantum computing for financial institutions

The financial institution might use a cloud-based quantum simulator to model and test quantum algorithms for risk analysis and portfolio optimization tasks. This allows the institution to evaluate the potential benefits of quantum computing without investing in its quantum hardware.

Once the institution has found a promising quantum algorithm, it can test and improve it using a cloud-based quantum simulator. This can involve running simulations on different datasets and tweaking the algorithm to make it work better and more accurately.

Once the institution is satisfied with the performance of the quantum algorithm, it can use quantum hardware to run the algorithm on real data. The institution can then use the results of the quantum computation to help them make decisions or improve their risk analysis, portfolio optimization, or other financial operations.

Case study

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