Welcome to NISQ-Seg, the official repository for the paper "Qubit-efficient Variational Quantum Algorithms for Image Segmentation", accepted at the Quantum Computing and Engineering 2024 (QCE'24) conference by IEEE. This repository provides the full pipeline for reproducing the experimental results and demonstrations of the three primary encoding methods introduced in the paper:
Image is converted to an undirected weighted graph with similarity of the pixels as the edge weight metric, then solving the minimum cut obtains the segmentation of the image.- Parametric Gate Encoding (PGE)
- Ancilla Basis Encoding (ABE)
- Adaptive Cost Encoding (ACE)
These quantum techniques are optimized for Noisy Intermediate-Scale Quantum (NISQ) devices and demonstrate efficient
qubit usage for graph-based image segmentation tasks. You can explore the different encoding methods by running the
provided Jupyter notebook (tutorial.ipynb
).
The preprint is available on arXiv. The original implementation (in Qiskit) is also available on GitHub
If you find this code useful in your research, please cite the following paper:
@INPROCEEDINGS{10821431,
author={Venkatesh, Supreeth Mysore and Macaluso, Antonio and Nuske, Marlon and Klusch, Matthias and Dengel, Andreas},
booktitle={2024 IEEE International Conference on Quantum Computing and Engineering (QCE)},
title={Qubit-Efficient Variational Quantum Algorithms for Image Segmentation},
year={2024},
volume={01},
pages={450-456},
doi={10.1109/QCE60285.2024.00059}
}
Supreeth Mysore Venkatesh
For any inquiries, please reach out to:
- Email: [email protected]
- LinkedIn: Supreeth Mysore Venkatesh
- Website: www.supreethmv.com