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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

QML algorithms

This discipline combines classical machine learning with quantum capabilities to produce better solutions. Enhancing ML algorithms and/or classical training with quantum resources broadens the scope of pure ML, as happens with some classical devices such as GPUs or TPUs.

It has been extensively reported that using quantum approaches in learning algorithms could have several advantages (reviewed by Schuld et al., 2018). However, most of the earliest research in this framework chased a decrease in computational complexity in conjunction with a speedup. Current investigations also study methods for quantum techniques to provide unconventional learning representations that could even outperform standard ML in the future.

In recent years, the theories and techniques of QC have evolved rapidly, and the potential benefits for real-world applications have become increasingly evident (Deb et al., 2021; Egger et al., 2021). How QC may affect ML is a key topic of research...

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