Deutsche Börse has piloted the use of quantum algorithms to compute risk models, finding that the technology can bring down the time required for simulations from years to hours.
The exchange operator worked with JoS Quantum to develop a quantum algorithm that could tackle some of the limitations facing its risk models for forecasting the financial impact of adverse external developments such as macroeconomic events, changes in competition, or new regulation.
Today, computation is done via traditional Monte Carlo simulation on existing off-the-shelf hardware, which – depending on the complexity of the model and the number of simulation parameters – can take days.
Deutsche Börse focussed on the speedup for up to 1000 inputs, which would require up to 10 years of Monte Carlo simulation.
The results “demonstrated that the application of quantum computing would drastically reduce the required computational effort and thus total calculation time. For the chosen benchmark of 1000 inputs the “warp factor” is about 200,000, reducing the off-the-shelf Monte-Carlo computation time of about 10 years to less than 30 minutes quantum computing time.”
The experiment then saw the model executed on IBM’s quantum machine although, due to hardware limitations, a smaller version of the model was run. However, the required hardware to run a full sensitivity analysis in production is likely to be available in a “few years”.
“Quantum hardware providers could and will possibly meet these requirements in the second half of this decade; meaning that a real-life application of quantum computing in risk management could only be a matter of a few years!” says the exchange operator.
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