Usage

Without any configuration needed, you can execute

kedro run --pipeline prepare

followed by

kedro run

and a default pipeline should run. In this and following examples the leading poetry run is omitted for simplicity.

Note that is required to always run the prepare pipeline in advance to any actual processing pipeline. This is because of the current implementation relies on dynamically created nodes that are depending on the configuration and therefore requiring two separate pipeline executions.

In summary, the following pipelines exist: - prepare : generates all possible combinations of configurations based on the current parameter set - measure : performs the actual time measurement by executing experiments for each of the previously generated configurations with the ability to parallelize processing - combine : gathers all the results from the measure pipeline and combines them into a single output dataset - visualize : takes the combined experiment results and generates your plots

The default pipeline covers measure, combine and visualize. You can run them separately by specifying the pipeline name.

This project can take advantage of multiprocessing to evaluate numerous combinations of qubits, depths and shots in parallel in the measure pipeline. To use this, you should explicitly call the individual pipelines. In summary the whole experiment will then look as follows:

kedro run --pipeline prepare
kedro run --pipeline measure --runner quafel.runner.Parallel
kedro run --pipeline combine
kedro run --pipeline visualize

Here, only the pipeline measure will utilize multiprocessing and the rest will run single process. We recommend this approach since there is no advantage by running the other pipelines in parallel as well. Of course, you can run the measure pipeline in a single process as well by omitting the --runner option. If for some reason the execution of the measure pipeline gets interrupted, running the same pipeline again without running prepare will allow re-using previously generated artefacts.

For details on the output, see the Data Structure Section.

Note that if you want to re-run e.g. the visualize pipeline, you have to re-run the prepare pipeline as well. This is because intermediate data containing information about the partitions is being deleted after the visualize pipeline of an experimant successfully ran. This constraint will be removed in future releases.