Quantum Deep Hedging

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Quantum Deep Hedging PlatoBlockchain Data Intelligence. Vertical Search. Ai.

El Amine Cherrat^1,2, Snehal Raj¹, Iordanis Kerenidis^1,2, 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.

► BibTeX data

@article{Cherrat2023quantumdeephedging, doi = {10.22331/q-2023-11-29-1191}, url = {https://doi.org/10.22331/q-2023-11-29-1191}, title = {Quantum {D}eep {H}edging}, author = {Cherrat, El Amine and Raj, Snehal and Kerenidis, Iordanis and Shekhar, Abhishek and Wood, Ben and Dee, Jon and Chakrabarti, Shouvanik and Chen, Richard and Herman, Dylan and Hu, Shaohan and Minssen, Pierre and Shaydulin, Ruslan and Sun, Yue and Yalovetzky, Romina and Pistoia, Marco}, journal = {{Quantum}}, issn = {2521-327X}, publisher = {{Verein zur F{“{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}}, volume = {7}, pages = {1191}, month = nov, year = {2023}<br /> }

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[2] 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).

[3] 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).

The above citations are from SAO/NASA ADS (last updated successfully 2023-11-30 01:36:35). The list may be incomplete as not all publishers provide suitable and complete citation data.

On Crossref’s cited-by service no data on citing works was found (last attempt 2023-11-30 01:36:34).

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