v1.7.0

This new release allows the user to configure price estimation problems for Cliquet options a more complicated financial derivative whose final payoff can be positive or negative.

The cliquet_return_estimation module from QQuantLib.finance package configures the Cliquet option problem, the Amplitude Estimation algorithm and a properly configured QPU. The price problem is solved and a pandas DataFrame with all the information is returned. In this case, the payoff is loaded completely into the quantum state (using square or direct encoding) so, only the RQAE algorithms will provide a correct solution if the final price is negative.

The cliquet_return_estimation_step_payoff from QQuantLib.finance package works like the previous model but the payoff is loaded by parts (positive and negative) and then post-process the output to provide the correct estimation. In this case, traditional AE algorithms will provide a correct solution if the final price is negative.

In the benchmark/q_ae_cliquet/ folder we provide the modules benchmark_cliquet and benchmark_cliquet_step_po. These scripts execute, from the command line, compute the price of a Cliquet option using a QAE algorithm. The configuration of the Cliquet, the AE algorithm and the QPU can be provided using JSON files. Several JSON file examples can be found in the folder. Additionally, the QAE_CliquetOptions.ipynb jupyter notebook that serves as the tutorial is placed inside it.