Quantum Deep Hedging

El Amine Cherrat, Snehal Raj, Iordanis Kerenidis, Abhishek Shekhar, Ben Wood, Jon Dee, Shouvanik Chakrabarti, Richard Chen, Dylan Herman, Shaohan Hu, Pierre Minssen, Ruslan Shaydulin, Yue Sun, Romina Yalovetzky and Marco Pistoia
Additional contact information
El Amine Cherrat: QCW - Q.C. Ware
Snehal Raj: QCW - Q.C. Ware
Iordanis Kerenidis: QCW - Q.C. Ware
Abhishek Shekhar: JP Morgan AI Research
Ben Wood: JP Morgan AI Research
Jon Dee: JP Morgan AI Research
Shouvanik Chakrabarti: JP Morgan AI Research
Richard Chen: JP Morgan AI Research
Dylan Herman: JP Morgan AI Research
Shaohan Hu: JP Morgan AI Research
Pierre Minssen: JP Morgan AI Research
Ruslan Shaydulin: JP Morgan AI Research
Yue Sun: JP Morgan AI Research
Romina Yalovetzky: JP Morgan AI Research
Marco Pistoia: JP Morgan AI Research

Post-Print from HAL

Abstract: Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance. In our work we look at the problem of hedging where deep reinforcement learning offers a powerful framework for real markets. We develop quantum reinforcement learning methods based on policy-search and distributional actor-critic algorithms that use quantum neural network architectures with orthogonal and compound layers for the policy and value functions. We prove that the quantum neural networks we use are trainable, and we perform extensive simulations that show that quantum models can reduce the number of trainable parameters while achieving comparable performance and that the distributional approach obtains better performance than other standard approaches, both classical and quantum. We successfully implement the proposed models on a trapped-ion quantum processor, utilizing circuits with up to $16$ qubits, and observe performance that agrees well with noiseless simulation. Our quantum techniques are general and can be applied to other reinforcement learning problems beyond hedging.

Date: 2023-03-29
References: Add references at CitEc
Citations:

Published in Quantum, 2023, 7, pp.1191. ⟨10.22331/q-2023-11-29-1191⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04263807

DOI: 10.22331/q-2023-11-29-1191

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().