ae_price_estimation

This module implements the ae_price_estimation function that allows to the user configure a price estimation problem using financial parameters, encode the expected value integral to compute in a quantum state and estimate it using the different AE algorithms implemented in the QQuantLib.AE package.

This function uses the DensityProbability and the PayOff classes (from finance.probability_class and finance.payoff_class modules respectively) for defining the option price estimation problem. Then the q_solve_integral function (from finance.quantum_integration module) is used for computing the expected value integral.

The function deals with all the mandatory normalisations for returning the desired price estimation.

Authors: Alberto Pedro Manzano Herrero & Gonzalo Ferro Costas

QQuantLib.finance.ae_price_estimation.ae_price_estimation(**kwargs)

Configures an option price estimation problem and solving it using AE integration techniques

Parameters:

kwargs (dictionary.) – Dictionary for configuring the price estimation problem, the encoding of the price estimation data into the quantum circuit and the AE integration technique for solving it.

Note

The keys for the input kwargs dictionary will be the necessary keys for configuring the DensityProbability class (see QQuantLib.finance.probability_class), the PayOff class (see QQuantLib.finance.payoff_class) and the q_solve_integral function (see QQuantLib.finance.quantum_integration).

Returns:

pdf – DataFrame with the configuration of the AE problem and the solution

Return type:

Pandas DataFrame