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Financial Modeling Using Quantum Computing

Financial Modeling Using Quantum Computing

By : Anshul Saxena, Javier Mancilla, Iraitz Montalban, Christophe Pere
5 (8)
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Financial Modeling Using Quantum Computing

Financial Modeling Using Quantum Computing

5 (8)
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)
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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

Conclusion

As mentioned earlier, even if accuracy is a common measure from the classification report that most people will look at, the way to treat this kind of imbalanced data scenario is to compare the models using a balanced accuracy score, or AUC score, which in this case are the same, since it is a binary classification challenge.

Figure 6.8 – A comparison of classification results between classical and hybrid quantum-classical methods

Figure 6.8 – A comparison of classification results between classical and hybrid quantum-classical methods

At first glance, the results do not appear to be conclusive about the benefits of using hybrid quantum-classical for classification problems that the finance sector may face. However, the purpose of the exercise in this chapter is to get people to think about their own business challenges and do more research, since we can see that quantum machine learning could be at least equal or slightly better than classical ML methods (e.g., QSVC versus SVC). When any incremental benefit is achieved in terms of...

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