README.md

Cut&Shoot

Cut&Shoot is a tool to execute quantum circuits by applying circuit cutting and shot-wise distribution in pipeline.

The pipeline consists of four main steps:

1. Cut: The original circuit is divided into smaller fragments using the chosen cutting tool. The tool outputs all possible fragment variations, which are then treated as standalone quantum circuits with their respective shot allocation determined by a custom allocation policy.
2. Split: The shots for each fragment variation are distributed across the target NISQ devices according to the selected split policy. The shots of each fragment variation are executed independently and concurrently on the target QPUs.
3. Merge: The execution results from the NISQ devices are merged to obtain the probability distribution for each fragment through the user-specified merge policy, which may differ from the split policy.
4. Sew: Finally, the probability distributions of the fragments are combined to reconstruct the original circuit's probability distribution.

Installation

Cut&Shoot can be installed using poetry:

git clone [this repository]
cd Cut-Shoot
poetry shell
poetry install

Usage

Cut&Shoot can be used as a Python library. The following example shows how to use Cut&Shoot as a Python library:

from run import run

run(
    circuit,
    shots_assignment,
    observable_string,
    cut_tool,
    provider_backend_couple,
    split_func,
    merg_func,
    cutting = True,
    shot_wise = True,
    metadata,
)

passing the following parameters:

• circuit_qasm: the quantum circuit in QASM format.
• shots_assignment: the policy to assign shots to each fragment.
• observable_string: a string representing the observable to measure.
• cut_tool: the cutting tool to use and its parameters.
• provider_backend_couple: a list of tuples, each containing a provider and a backend.
• split_func: the policy to split shots across the QPUs.
• merge_func: the policy to merge the results from the QPUs.
• cutting: a boolean indicating whether to perform the cutting step.
• shot_wise: a boolean indicating whether to perform the shot-wise distribution step.
• metadata: a dictionary containing additional information.

Run the experiments

To run the experiments, execute the following command inside the ./exp folder:

python exp.py run [config_file]

where config_file is the path to the configuration file.