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In finance, a European call gives the buyer the right to buy some underlying asset such as a stock at some pre-determined strike price at a specific expiration date. The Black-Scholes equation is a fairly simple formula that can be easily written in Python for classical computing to value financial derivatives like the call option.

At the link below, IBM's cross-displinary quantum package for Python, Qiskit Aqua, seems to use qubits to price a European call option instead. Could someone give me a walk-through of what the code is doing line by line and how it compares to a classical Python code for pricing a European call? Are there any theoretical or numerical differences between classical and quantum pricing?

https://github.com/Qiskit/qiskit-aqua/blob/master/qiskit/finance/components/uncertainty_problems/european_call_expected_value.py

Also, is there an article being used as the source for the above Python script? My guess is that it's this one, but not sure since I don't yet have a background in matching quantum code with quantum formulas:

"Towards Pricing Financial Derivatives with an IBM Quantum Computer" https://arxiv.org/pdf/1904.05803.pdf

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  • $\begingroup$ It seems that you are interested in application of quantum computers in finance. This article might be interesting for you: arxiv.org/abs/1806.06893 (Quantum Risk Analysis) $\endgroup$ – Martin Vesely Nov 15 at 14:10
  • $\begingroup$ quantum VaR and CVaR. Thanks, i saw the video interview of Woerner the other week but didn't look at the paper yet $\endgroup$ – develarist Nov 15 at 14:45
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There are tutorials for a lot of the Qiskit Aqua functions kept in the tutorials repository, and I think this talks about the finance problem you are interested in.

All of these tutorials are also available on the IBM Q Experience where you can run them in a browser.

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