JPMorgan Chase and QC Ware Evolve Hedging for a Quantum Future

JPMorgan Chase and QC Ware Evolve Hedging for a Quantum Future

Insider Brief

PRESS RELEASE — QC Ware and JPMorgan Chase have completed a study of quantum “deep hedging,” paving the way for future increased risk mitigation capabilities in financial services.

In a new paper released March 30th, JPMorgan Chase and QC Ware examined two questions on how the practice of deep hedging—reducing risk for a portfolio utilizing data driven models that consider market frictions and trading constraints—might be improved with quantum computing. 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. “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, Head of Global Technology Applied Research, JPMorgan Chase. “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.”

Read the full research paper here.

Matt Swayne

Matt Swayne

With a several-decades long background in journalism and communications, Matt Swayne has worked as a science communicator for an R1 university for more than 12 years, specializing in translating high tech and deep tech for the general audience. He has served as a writer, editor and analyst at The Quantum Insider since its inception. In addition to his service as a science communicator, Matt also develops courses to improve the media and communications skills of scientists and has taught courses. matt@thequantuminsider.com

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