Quantum Deep Hedging

El Amine Cherrat1,2, Snehal Raj1, Iordanis Kerenidis1,2, Abhishek Shekhar3, Ben Wood3, Jon Dee3, Shouvanik Chakrabarti4, Richard Chen4, Dylan Herman4, Shaohan Hu4, Pierre Minssen4, Ruslan Shaydulin4, Yue Sun4, Romina Yalovetzky4, and Marco Pistoia4

1QC Ware
2Université de Paris, CNRS, IRIF
3Quantitative Research, JPMorgan Chase
4Global Technology Applied Research, JPMorgan Chase

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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.

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Cited by

[1] Dylan Herman, Cody Googin, Xiaoyuan Liu, Yue Sun, Alexey Galda, Ilya Safro, Marco Pistoia, and Yuri Alexeev, "Quantum computing for finance", Nature Reviews Physics 5 8, 450 (2023).

[2] Junyu Liu, "Towards real-world implementations of quantum machine learning", Quantum Views 7, 77 (2023).

[3] Enrico Fontana, Dylan Herman, Shouvanik Chakrabarti, Niraj Kumar, Romina Yalovetzky, Jamie Heredge, Shree Hari Sureshbabu, and Marco Pistoia, "The Adjoint Is All You Need: Characterizing Barren Plateaus in Quantum Ansätze", arXiv:2309.07902, (2023).

[4] M. Cerezo, Martin Larocca, Diego García-Martín, N. L. Diaz, Paolo Braccia, Enrico Fontana, Manuel S. Rudolph, Pablo Bermejo, Aroosa Ijaz, Supanut Thanasilp, Eric R. Anschuetz, and Zoë Holmes, "Does provable absence of barren plateaus imply classical simulability? Or, why we need to rethink variational quantum computing", arXiv:2312.09121, (2023).

[5] Alexandr Sedykh, Maninadh Podapaka, Asel Sagingalieva, Karan Pinto, Markus Pflitsch, and Alexey Melnikov, "Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes", arXiv:2304.11247, (2023).

[6] Travis L. Scholten, Carl J. Williams, Dustin Moody, Michele Mosca, William Hurley, William J. Zeng, Matthias Troyer, and Jay M. Gambetta, "Assessing the Benefits and Risks of Quantum Computers", arXiv:2401.16317, (2024).

The above citations are from Crossref's cited-by service (last updated successfully 2024-02-26 17:30:03) and SAO/NASA ADS (last updated successfully 2024-02-26 17:30:04). The list may be incomplete as not all publishers provide suitable and complete citation data.