About

The Python code for the paper A Hybrid Quantum-Classical Algorithm for Robust Fitting

The demo program was only tested under Conda in a standard computer with Ubuntu 20.04.

If you find our code is useful, please cite:

@article{Dzung22hqc,
  title = {A Hybrid Quantum-Classical Algorithm for Robust Fitting},
  author = {Anh-Dzung Doan and Michele Sasdelli and David Suter and Tat-Jun Chin},
  booktitle =  {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022}
}

Installation

• Python 3.7
• numpy 1.21 (pip install numpy)
• matplotlib 3.4 (pip install matplotlib)
• pickle5 (pip install pickle5)
• D-Wave Ocean (pip install dwave-ocean-sdk)
• qubovert (pip install qubovert)
• Gurobi (python -m pip install gurobipy==9.1.2)
• opencv (pip install opencv-python)

Usage

• Synthetic data (script main_synthetic.py)

  1. (Optional) Register an account on D-Wave Leap (https://cloud.dwavesys.com/leap/login/?next=/leap/), obtain TOKEN of D-Wave Leap, and assign its value to the variable TOKEN within the script.
  2. Run script

  Note: if TOKEN of D-Wave Leap is provided (step 1), QUBO will be solved by quantum annealing, otherwise QUBO will be solved by simulated annealing

• Real data (script main_fund.py)

  • Simply run script (It will likely take around 30 minutes)
  • Note: To normalise image coordiates, we used normalise2dpts.m of Peter Kovesi's toolbox