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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

Challenges of quantum implementations on cloud platforms

As we mentioned previously, most of the examples shown previously leverage the fact that quantum computing can be mimicked by our classical resources (using simulators). As an example, in Chapter 5, we used the following routine:

backend = Aer.get_backend('qasm_simulator')
result = execute(quantum_circuit, backend, shots=10).result()
counts  = result.get_counts(quantum_circuit)

We utilized a qasm_simulator, an implementation that can execute the operations defined in our quantum circuit and provide the expected outcome as dictated by the mathematical principles governing quantum computing.

We would like to take this very same circuit to a quantum computer, but it is not as easy as purchasing one on Amazon. We can purchase commercially available devices, but the price might be too high for most organizations.

D-Wave

In 2011, D-Wave Systems announced the world’s first commercially available...

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