CORRECTING and REPLACING IonQ and Fidelity Center for Applied

IonQ, Inc, the leader in quantum computing, announced the release of a new paper in collaboration with Fidelity Center for Applied Technology (FCAT) that demonstrates how its quantum computers can outperform classical computers to generate high-quality data for use in testing financial models. Financial institutions commonly use models for asset allocation, electronic trading, and pricing, and require testing data to validate the accuracy of these models. The new technique, demonstrated by FCAT on IonQ's latest quantum computers, has the potential to be the first class of quantum machine learning models to be deployed for broad commercial use. "At FCAT, we track new and emerging technologies and trends to help Fidelity meet the changing needs of our customers and ass These classical approaches are often limited because real-world dependencies between variables–for example, in a portfolio of stocks–are too complex for them to model. IonQ and FCAT demonstrated that data generated with quantum machine learning algorithms is more representative of these real-world dependencies and is therefore better at accounting for edge cases like black swan events. The technique invented by IonQ and FCAT leverages copulas, a method often used in statistical models to describe relationships between large numbers of variables. For instance, large financial institutions use copulas to understand relationships between stock prices (if the price of X is within a particular range, then the price of Y tends to go up). By using quantum computers to implement copulas, IonQ and FCAT demonstrated the ability to construct complex models beyond the capability of classical computers. "This research, performed on IonQ hardware, shows quite clearly that leveraging quantum computing can lead to superior financial modeling results.