Quantum Computing Application Benchmark (QUARK) is a framework for orchestrating benchmarks of different industry applications on quantum computers. QUARK supports various applications, like the traveling salesperson problem (TSP), the maximum satisfiability (MaxSAT) problem, or the robot path optimization in the PVC sealing use case. It also features different solvers (e.g., simulated /quantum annealing and the quantum approximate optimization algorithm (QAOA)), quantum devices (e.g., IonQ and Rigetti), and simulators. It is designed to be easily extendable in all of its components: applications, mappings, solvers, devices, and any other custom modules.

Details about the motivations for the original framework can be found in the accompanying QUARK paper from Finžgar et al. Even though the architecture changes significantly from QUARK 1.0 to 2.0, the guiding principles still remain. The most recent publication from Kiwit et al. provides an updated overview of the functionalities and quantum machine learning features of QUARK 2.0.

Documentation with a tutorial and developer guidelines can be found here: https://quark-framework.readthedocs.io/en/dev/.

As this framework is implemented in Python 3.9, you need to install this version of Python if you do not already have it installed. Other versions could cause issues with other dependencies used in the framework. Additionally, we rely on several pip dependencies, which you can install in two ways:

1. Install pip packages manually, or
2. Use the QUARK installer.

For this installer to work, you need to install the following packages in the first place:

• inquirer==3.1.2
• pyyaml==6.0
• packaging==23.1

To limit the number of packages you need to install, there is an option to only include a subselection of QUARK modules. You can select the modules of choice via:

python src/main.py env --configure myenv

Of course there is a default option, which will include all available options.

Depending on your configured modules, you will need to install additional Python packages, as the above-mentioned 3 packages are not sufficient to run a benchmark! We provide the option to generate a Conda file or a pip requirements file, which you can use to install the required packages. You can also configure multiple QUARK environments and then switch between them via:

python src/main.py env --activate myenv2

Note: Different modules require different python packages. Be sure that your python environment has the necessary packages installed!

To see which environments are configured, please use

python src/main.py env --list

You can also visualize the contents of your QUARK environment:

(quark) %  python src/main.py env --show myenv
[...]
Content of the environment:
>-- TSP
    >-- GreedyClassicalTSP
        >-- Local

In case you want to use custom modules files (for example, to use external modules from other repositories), you can still use the --modules option. You can find the documentation in the respective Read the Docs section.

export HTTP_PROXY=http://username:[email protected]:8080 
export AWS_PROFILE=quantum_computing
export AWS_REGION=us-west-1
python src/main.py

HTTP_PROXY is only needed if you have to use a proxy to access AWS.

AWS_PROFILE is only needed if you want to use an AWS braket device (default is quantum_computing). In case no profile is needed in your case, please set export AWS_PROFILE=default.

AWS_REGION is only needed if you need another aws region than us-east-1. Usually this is specific to the Braket device.

Example run (You need to check at least one option with an X for the checkbox question):

(quark) % python src/main.py 
[?] What application do you want?: TSP
   PVC
   SAT
 > TSP

2023-03-21 09:18:36,440 [INFO] Import module modules.applications.optimization.TSP.TSP
[?] (Option for TSP) How many nodes does you graph need?:
 > [X] 3
   [ ] 4
   [ ] 6
   [ ] 8
   [ ] 10
   [ ] 14
   [ ] 16

[?] What submodule do you want?:
   [ ] Ising
   [ ] Qubo
 > [X] GreedyClassicalTSP
   [ ] ReverseGreedyClassicalTSP
   [ ] RandomTSP

2023-03-21 09:18:49,563 [INFO] Skipping asking for submodule, since only 1 option (Local) is available.
2023-03-21 09:18:49,566 [INFO] Submodule configuration finished
[?] How many repetitions do you want?: 1
2023-03-21 09:18:50,577 [INFO] Import module modules.applications.optimization.TSP.TSP
2023-03-21 09:18:50,948 [INFO] Created Benchmark run directory /Users/user1/QUARK/benchmark_runs/tsp-2023-03-21-09-18-50
2023-03-21 09:18:51,025 [INFO] Codebase is based on revision 075201825fa71c24b5567e1290966081be7dbdc0 and has some uncommitted changes
2023-03-21 09:18:51,026 [INFO] Running backlog item 1/1, Iteration 1/1:
2023-03-21 09:18:51,388 [INFO] Route found:
 Node 0 ->
 Node 2 ->
 Node 1
2023-03-21 09:18:51,388 [INFO] All 3 nodes got visited
2023-03-21 09:18:51,388 [INFO] Total distance (without return): 727223.0
2023-03-21 09:18:51,388 [INFO] Total distance (including return): 1436368.0
2023-03-21 09:18:51,389 [INFO]
2023-03-21 09:18:51,389 [INFO]  ============================================================
2023-03-21 09:18:51,389 [INFO]
2023-03-21 09:18:51,389 [INFO] Saving 1 benchmark records to /Users/user1/QUARK/benchmark_runs/tsp-2023-03-21-09-18-50/results.json
2023-03-21 09:18:51,746 [INFO] Finished creating plots.

All used config files, logs and results are stored in a folder in the benchmark_runs directory.

It is also possible to start the script with a config file instead of using the interactive mode:

 python src/main.py --config config.yml

Note: This should only be used by experienced users as invalid values will cause the framework to fail!

Example for a config file:

application:
  config:
    nodes:
    - 3
  name: TSP
  submodules:
  - config: {}
    name: GreedyClassicalTSP
    submodules:
    - config: {}
      name: Local
      submodules: []
repetitions: 1

We also support the option to run the framework as a container. After making sure your docker daemon is running, you can run the container:

docker run -it --rm ghcr.io/quark-framework/quark

You can also build the docker image locally like:

docker build -t ghcr.io/quark-framework/quark .

In case you want to use a config file you have to add it to the docker run command:

-v /Users/alice/desktop/my_config.yml:/my_config.yml

/Users/alice/desktop/my_config.yml specifies the QUARK config file on your local machine. Then you can run the docker container with the config:

docker run -it --rm  -v /Users/alice/desktop/my_config.yml:/my_config.yml ghcr.io/quark-framework/quark --config my_config.yml

In case you want to access the benchmark run folder afterwards, you can attach a volume to the run command:

-v /Users/alice/desktop/benchmark_runs:/benchmark_runs/

The results of the benchmark run are then stored to a new directory in /Users/alice/desktop/benchmark_runs.

In case you have local proxy settings you can add the following flags to the run command:

-e http_proxy=$http_proxy -e https_proxy=$https_proxy -e HTTP_PROXY=$HTTP_PROXY -e HTTPS_PROXY=$HTTPS_PROXY

AWS credentials can be mounted to the run command like:

-v $HOME/.aws/:/root/.aws:ro

You can also summarize multiple existing experiments like this:

python src/main.py --summarize quark/benchmark_runs/2021-09-21-15-03-53 quark/benchmark_runs/2021-09-21-15-23-01

This allows you to generate plots from multiple experiments.

This project is licensed under Apache License 2.0.