Book Image

Financial Modeling Using Quantum Computing

By : Anshul Saxena, Javier Mancilla, Iraitz Montalban, Christophe Pere
5 (1)
Book Image

Financial Modeling Using Quantum Computing

5 (1)
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)
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

Error mitigation

Some common sources of error can be more systematically tackled since measuring the classical outcome of quantum hardware is not free of errors. Luckily, this type of error can be tackled by observing the common errors that are made upon readout and compensating for post-processing the outcome.

If we look into our IBM Quantum Experience service once more, we could request the readout error for a given device. In Figure 9.6, we can observe how any operation that’s done on qubits 10 and 15, upon measurement, could be misinterpreted:

Figure 9.6 – Readout error on IBM’s Toronto device (27 superconducting qubits Falcon r4)

Figure 9.6 – Readout error on IBM’s Toronto device (27 superconducting qubits Falcon r4)

These statistics can be derived by the simple act of placing an operation whose outcome is known (for example, X|ψ) and recording the discrepancies upon measuring it for a significant number of tryouts. If those statistics are known, you can compensate for the measurements that are obtained...