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?


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

  • $\begingroup$ 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) $\endgroup$ Nov 15, 2019 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, 2019 at 14:45

1 Answer 1


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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