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Quantum Physics

arXiv:2109.04298 (quant-ph)
[Submitted on 9 Sep 2021]

Title:Quantum Machine Learning for Finance

Authors:Marco Pistoia, Syed Farhan Ahmad, Akshay Ajagekar, Alexander Buts, Shouvanik Chakrabarti, Dylan Herman, Shaohan Hu, Andrew Jena, Pierre Minssen, Pradeep Niroula, Arthur Rattew, Yue Sun, Romina Yalovetzky
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Abstract:Quantum computers are expected to surpass the computational capabilities of classical computers during this decade, and achieve disruptive impact on numerous industry sectors, particularly finance. In fact, finance is estimated to be the first industry sector to benefit from Quantum Computing not only in the medium and long terms, but even in the short term. This review paper presents the state of the art of quantum algorithms for financial applications, with particular focus to those use cases that can be solved via Machine Learning.
Subjects: Quantum Physics (quant-ph); Machine Learning (cs.LG)
Cite as: arXiv:2109.04298 [quant-ph]
  (or arXiv:2109.04298v1 [quant-ph] for this version)
  https://doi.org/10.48550/arXiv.2109.04298

Submission history

From: Shaohan Hu [view email]
[v1] Thu, 9 Sep 2021 14:20:10 UTC (140 KB)
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