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https://doi.org/10.1103/physre...
Article . 2018 . Peer-reviewed
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https://dx.doi.org/10.48550/ar...
Article . 2018
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Quantum computational finance: Monte Carlo pricing of financial derivatives

Authors: Brajesh Gupt; Thomas R. Bromley; Patrick Rebentrost;

Quantum computational finance: Monte Carlo pricing of financial derivatives

Abstract

Financial derivatives are contracts that can have a complex payoff dependent upon underlying benchmark assets. In this work, we present a quantum algorithm for the Monte Carlo pricing of financial derivatives. We show how the relevant probability distributions can be prepared in quantum superposition, the payoff functions can be implemented via quantum circuits, and the price of financial derivatives can be extracted via quantum measurements. We show how the amplitude estimation algorithm can be applied to achieve a quadratic quantum speedup in the number of steps required to obtain an estimate for the price with high confidence. This work provides a starting point for further research at the interface of quantum computing and finance.

18 pages, 4 figures, updated final version. Published in PRA

Keywords

Quantum Physics, FOS: Physical sciences, Quantum Physics (quant-ph)

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
147
Top 1%
Top 1%
Top 1%
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