IonQ and Fidelity Center for Applied Technology Demonstrate Quantum Machine Learning for Finance on IonQ Quantum Computers
IonQ, a leader in quantum computing, has collaborated with Fidelity Center for Applied Technology (FCAT) to develop quantum machine learning algorithms that significantly outperform classical methods in financial analysis. The new algorithms utilize copulas to capture complex relationships between variables, enhancing data quality for financial modeling in areas such as asset allocation and electronic trading. Demonstrated on IonQ's latest quantum computers, these advancements signal a transformative potential for financial institutions, enabling better risk management and portfolio optimization.
- Quantum algorithms significantly outperform classical algorithms in financial analysis.
- Demonstrated on IonQ's latest quantum computer, showcasing real-world applicability.
- Potential for broad commercial deployment of quantum machine learning in finance.
- None.
- New paper describes how quantum machine learning algorithms have exponential advantage over classical counterparts in financial analysis
- Algorithm leverages copulas--a common data analysis technique--to better describe the complex relationships between several variables, such as stock prices
- Algorithm has been demonstrated on IonQ’s latest quantum computer and outperformed equivalent algorithm based on classical machine learning
- Quantum algorithms can be applied to statistical problems in several industries, expanding the near-term opportunity for quantum computing
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 modeling 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,” said
The copula method underlying FCAT and IonQ’s work can potentially be applied to any industry dealing with complex systems that involve several correlated variables. In the near future, it is expected that quantum machine learning may be applied to climate research, medical imaging, and recommendation systems. In finance, the first quantum machine learning methods using copulas are likely to be applied to risk management and portfolio optimization.
“At FCAT, we track new and emerging technologies and trends to help Fidelity meet the changing needs of our customers and associates,” said
The news continues a year of considerable momentum for IonQ. Its trapped-ion quantum computers were recently added to
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