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Quantum Deep Hedging

Financial Industry  •  Quantum Machine Learning

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JP Morgan Chase

An NYC-based multinational financial services corporation, the largest bank in the United States and the world's largest by market capitalization.

In 2023, JP Morgan Chase developed and tested new algorithms to consider more future scenarios and variables in options pricing, with a marked improvement over the industry standard.

Challenges

Solution

Results

Classical hedging models that minimize risk in option markets cannot account for all possible scenarios and variables

Partnered with QC Ware and a university to develop and test new trainable quantum neural networks and a deep reinforcement learning algorithm on a Quantinuum machine

>10% improvement over the industry standard Black-Scholes method, and results were found to agree with simulations.

Challenges

Classical hedging models that minimize risk in option markets cannot account for all possible scenarios and variables

Solution

Partnered with QC Ware and a university to develop and test new trainable quantum neural networks and a deep reinforcement learning algorithm on a Quantinuum machine

Results

>10% improvement over the industry standard Black-Scholes method, and results were found to agree with simulations.

“We’re glad to be able to further optimize already sterling hedging strategies, not only to deliver value for investors, but also to allow for more frequent and sophisticated hedging of positions in the market.” - Dr. Marco Pistoia, Head of Global Technology Applied Research and Head of Quantum Computing