QC Ware and J.P. Morgan have completed a study of quantum deep hedging, a term used to describe reducing risk for a portfolio utilizing data driven models that consider market frictions and trading constraints.
The researchers first examined whether existing classical deep hedging frameworks could be improved using quantum deep learning. Then, using quantum reinforcement learning, they studied whether a new quantum framework could be defined for deep hedging.
The study found that deep hedging on classical frameworks using quantum deep learning enabled models to be trained more efficiently. The research, conducted on Quantinuum’s H1-1 quantum computer, also demonstrated the potential for future computational speed-ups, which could be implemented on noisy intermediate-scale quantum (NISQ) hardware.
Deep hedging on new quantum frameworks also enabled quantum value functions to:
- Efficiently learn the expectation and distribution of returns
- Offer improved performance via a quantum actor-critic reinforcement learning model
- Appropriately train quantum policies.
The quantum application could offer improvements for deep hedging in both classical and quantum environments — it leverages quantum machine learning methods to improve at times accuracy and trainability on high-performance GPU hardware, which will be helpful in financial services as quantum computing becomes more commercially accessible.
“We are taking deep hedging to its next logical evolutionary step,” said Iordanis Kerenidis, head of Quantum Algorithms at QC Ware, in a statement. “The results achieved with JPMorgan Chase demonstrate the huge potential and applicability of quantum machine learning, both today, by using quantum ideas to provide novel models with classical hardware, and also leveraging the continuously more powerful quantum hardware we anticipate in the future.”
“As quantum computing continues to mature, JPMorgan Chase’s leading position will only be further solidified via future-ready algorithms that will produce continually improving results,” said Marco Pistoia, managing director and head of Global Technology Applied Research at J.P. Morgan, in a statement. “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. This work helps to pave the way for the bank to incorporate quantum computing into its deep hedging.”