New IonQ/FCAT paper shows how quantum machine learning algorithms have exponential advantage over classical counterparts in financial analysis
New paper describes how quantum machine learning algorithms have IonQ, a specialist in quantum computing, has released a new paper in collaboration with Fidelity Center for Applied Technology (FCAT) demonstrating 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.
Today, many financial institutions generate data with classical machine learning to test their financial models. 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 modelling results. The application of quantum machine learning to other industries, ranging from climate science to geopolitics, means that a quantum-shaped future is just around the corner,” says Peter Chapman, CEO and President of IonQ. “Fidelity has long been a leader in understanding how new technologies will shape markets and industries, and we’re excited to work with them in this space.”
The copula method underlying FCAT and IonQ’s work can be applied to any industry dealing with complex systems that involve several correlated variables. In the near future, quantum machine learning may be applied to climate research, medical imaging, or recommendation systems. In finance, the first quantum machine learning methods using copulas are likely to be applied to risk management and portfolio optimisation.
“At FCAT, we track new and emerging technologies and trends to help Fidelity meet the changing needs of our customers and associates,” says Adam Schouela, Head of Emerging Technology, Fidelity Center for Applied Technology. “Classical computing enabled breakthroughs in the financial services space, and we expect quantum computing’s impact to be no less significant. We’re thrilled that our latest research with IonQ can help demonstrate quantum’s potential in this space.”
The news continues a year of considerable momentum for IonQ. Its trapped-ion quantum computers were recently added to Google Cloud Marketplace, making IonQ the only supplier whose quantum computers are available via all of the major cloud providers. In addition, IonQ’s co-founders joined the White House’s National Quantum Initiative Advisory Committee to accelerate the development of the national strategic technological imperative.