README.md

Addressing the Configuration Problem via Quantum Computing

Installation

Install python 3.9. Ideally, you should use a virtual environment for this project. Clone (or download) this repository and install the requirements via pip:

pip install -r requirements.txt

Quantum Approximate Optimization Algorithm (QAOA)

This repository contains code and data for applying QAOA to different instances and solving configuration selection and prioritization problems.

There are multiple Jupyter notebooks in this repository (all inside the configproblem/qaoa directory):

• qaoa_mincost_sat.ipynb - QAOA for Minimum Cost 2-SAT explaining the algorithm for QUBO formulation
• qaoa_mincost_k_sat.ipynb - QAOA for Minimum Cost k-SAT explaining the algorithm for more general PUBO formulation
• qaoa_instance_evaluation.ipynb - Generating different plots and histograms for evaluating results of QAOA

The first two notebooks contain detailed explanations of the algorithm and examples.

The accompanying python file of the first two notebooks (qaoa_mincost_sat.py and qaoa_mincost_k_sat.py) contains the different implementations of the phase seperating operator $U_C$ for the QUBO / PUBO formulation.

Additionally, you can use evaluation.py to run the QAOA algorithm on a set of DIMACS files and save and process the results.

Grover's Algorithm

This repository contains code and data for applying Grover's Algorithm to retrieve uniform random configuration samples. See the Jupyter notebook grover_sat.ipynb.

Usage

1. Navigate to the root directory of this repository inside a terminal.
2. If you have a virtual environment set up, activate it.

Notebooks

3. Run the jupyter notebook command. A browser window should open up.
4. In the browser window, navigate to the configproblem directory for the notebook about Grover's algorithm or to the configproblem/qaoadirectory for the notebooks about QAOA and open the notebook you want to run.

Evaluation of QAOA using DIMACS files

Run python -m configproblem.qaoa.evaluation <args> with the following arguments:

• --first <index> - Index of the first file to evaluate
• --last <index> - Index of the last file to evaluate
• --skip-quso - Skip running QAOA for the QUSO/QUBO formulation
• --skip-puso - Skip running QAOA for the PUSO/PUBO formulation
• --save-individual-results - Save the results of each file in a separate file (so you can terminate the program before it finishes)
• -f <filename> - Process the results of a previous run (saved in a file)
• --linux - Use this flag to use Linux file paths (default is Windows)

DIMACS files have to be placed in the benchmarks/qaoa-feature-models directory and have to be named feature_model_<index>.dimacs where <index> is the index of the file.

The files currently in the directory are the ones we used for our evaluation.

Results will be saved in benchmarks/qaoa-feature-models/results.

Other repositories

Here is a list of tools that we used during our evaluation:

• FeatureIDE for generating feature models.
• Qiskit for creating and simulating quantum circuits.
• Qubovert for conversion from and to QUBO and PUBO models.