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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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Part 1: Basic Applications of Quantum Computing in Finance
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Chapter 2: Quantum Machine Learning Algorithms and Their Ecosystem
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Part 2: Advanced Applications of Quantum Computing in Finance
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Part 3: Upcoming Quantum Scenario
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Chapter 8: Simulators and HPC’s Role in the NISQ Era
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Index
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References

Bennett, C. H., & Shor, P. W. (1998). Quantum information theory. IEEE transactions on information theory, 44(6), 2,724-2,742.

Biamonte, J., & Bergholm, V. (2017). Tensor networks in a nutshell. arXiv preprint arXiv:1708.00006.

Brassard, G., Hoyer, P., Mosca, M., & Tapp, A. (2002). Quantum amplitude amplification and estimation. Contemporary Mathematics, 305, 53-74.

Bowen, G. (2001). Classical information capacity of superdense coding. Physical Review A, 63(2), 022302.

Guerreschi, G. G., Hogaboam, J., Baruffa, F., & Sawaya, N. P. (2020). Intel Quantum Simulator: A cloud-ready high-performance simulator of quantum circuits. Quantum Science and Technology, 5(3), 034007.

Gray, J. (2018). quimb: A Python package for quantum information and many-body calculations. Journal of Open Source Software, 3(29), 819.

Kwak, Y., Yun, W. J., Kim, J. P., Cho, H., Park, J., Choi, M., ... & Kim, J. (2022). Quantum distributed deep learning architectures...

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