AER Quantum Server Plugin

This is the AER plugin for quantum local server. With it, you can easily run your AER jobs in a local server outside your own computer.

Functionalities

In this early stage, this plugin manages the following backends:

• AER (pure AER implementation)

and to them you can submit jobs to get: counts, quasi distributions and expectation values.

Even thought it's very limited for now, in future versions we aim to make more backends available.

Installation

To install run in your terminal:

pip install aer-plugin

No Server

Although this is meant to be used inside a docker container as a plugin for quantum local server, you can run it locally without it.

To do that, simply install the dependencies:

pip install -r requirements

and then import the aer_plugin module inside your python script:

#main.py

from aer_plugin import Plugin

p = Plugin()

# your qiskit code
# remember to export 
# your circuit to a 
# .qasm file
# ...

target_backend = "aer"
qasm_file_path = "<qasm_file_path>"
metadata = {"shots":1000}
result_type = "counts"

result = p.execute(
    target_backend, 
    qasm_file_path, 
    metadata, 
    result_type)

print(result) # e.g.: {'0':1000}

Usage

As postulated by the qserver documentation, the 3 possible outcomes are counts, quasi dist and expval. To evaluate them with this plugin you must provide some metadata in a specific pattern:

type	what you need
counts	no metadata is mandatory here, but you can specify the amount of shots you want metadata={"shots":1024}, by default the 1000 are taken.
quasi-dist	the same as counts
expval	the obs(observables) metadata is mandatory to run the estimator. You must provide a list of lists. Each list inside the main list represents a distinct pub (circuit evaluation - example: [ [pub1], [pub2], ..., [pubn] ]). Inside these lists, you must provide tuples with an observable and its coefficient like: (obs, coeff). Here's an example of a correct expval experiment metadata: [ [('ZZI', 0.4), ('ZIZ', 0.3)], [('IZ', 1)] ].

Dev

For developers. There's some dependencies you need to have installed before adding any code. To ease this installation, you can use conda/mamba/conda-lock to load everything in the correct version.

# using conda/mamba/conda-lock
mamba env create -f environment.yml
conda env create -f environment.yml
conda-lock install -n aer-plugin conda-lock.yml

# then activate your environment
conda activate aer-plugin
mamba activate aer-plugin

# however, if you prefere, you can use pip as well
# make sure to use a virtual environment to avoid conflicts
pip install -r requirements.txt -r dev-requirements.txt

In this project are being used 3 check stages to ensure code quality, which are:

• linting/code style: using pylint/black
• types: using mypy
• tests: using pytest

To manage all this, we're using tox, for 3 python versions: 3.10, 3.11 and 3.12, expecting more versions in the future.

During development, ensure to run tox regularly to ensure that everything is behaving as expected.

to learn more about tox check their wiki here.

Contributing

To start contributing for this project, make sure to:

• open an issue explaining what you have in mind
• follow the dev section
• add as many tests as you can on ./tests
• use a different branch for you modifications
• add comments explaining parts of your code that can be directly understand without deep investigation
• create readable code

Ensuring that, you're ready to open a pull request and be part of this community :)