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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

Infrastructure integration barrier

One of the natural barriers for companies looking to explore solutions with quantum computing is how to integrate them into their current operations. Depending on the case, the technology of real quantum hardware can be more or less prepared for real-time response and coexist with the current systems that the companies have deployed in the cloud. Particularly in the QML field, for classification challenges (credit scoring or fraud prediction), the instant response from a QC could be an issue to solve, since most of the machines have a queue system due to the small number of computers available. As a valid option, companies can use several types of simulators in the cloud to operate in a low range of qubits (most of the hybrid quantum-classical algorithms for QML operate quite well with a few tens of qubits) below the 40-qubit line.

The use of simulators can represent a good cost-efficient option, since the quantum algorithms can run faster (in...

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