https://github.com/kahsolt/wheelqnet
Official implementaion of WheelQNet, yet another toyish quantum binary classifier implemented in pyVQNet
https://github.com/kahsolt/wheelqnet
contest-solution quantum-classifier vqnet
Last synced: 11 months ago
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Official implementaion of WheelQNet, yet another toyish quantum binary classifier implemented in pyVQNet
- Host: GitHub
- URL: https://github.com/kahsolt/wheelqnet
- Owner: Kahsolt
- License: mit
- Created: 2023-12-17T08:39:39.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2024-01-05T09:51:11.000Z (about 2 years ago)
- Last Synced: 2025-01-03T16:28:36.461Z (about 1 year ago)
- Topics: contest-solution, quantum-classifier, vqnet
- Language: Python
- Homepage:
- Size: 1.72 MB
- Stars: 2
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# WheelQNet: Quantum Binary Classification via Rotation Averaging
Official implementaion of WheelQNet, yet another toyish quantum binary classifier implemented in pyVQNet
----
This repo contains code for the contest: 第一届量子信息技术与应用创新大赛 -- 本源量子VQNet量子机器学习大赛赛道
Contest page: [https://contest.originqc.com.cn/contest/32/contest:introduction](https://contest.originqc.com.cn/contest/32/contest:introduction)
Team Name: 做好坠机准备
Final Score: **84.6** (the 1st prize 😀)
| Model | Param cnt. | Train acc. | Test acc. |
| :-: | :-: | :-: | :-: |
| HEA | 32 | 78.608% | 82.178% |
| CCQC | 52 | 79.494% | 81.188% |
| CCQC-q | 52 | 80.253% | 78.218% |
| WheelQ | 32 | 77.342% | 79.208% |
| kNN-q | - | 81.392% | 87.129% |
⚠ only `CCQC` has 1 classical parameter, and `kNN-q` is non-parametrical, the other models are all pure quantum parametricalized :)
ℹ the proposed `WheelQNet` looks 花里胡哨 though, it just works!! 🎉
ℹ the proposed `kNN-q` looks good, but it may be our fortune 😂
### Quickstart
⚪ install
- `conda create -n vq python==3.8`
- `conda activare vq`
- `pip install -r requirements.txt`
⚪ run
- `python -m src.preprocess -f`, make feature data
- `python -m src.eval -L log\`, get testset predictions
- `python -m src.eval` for the default model (`knnq`)
- `run_vqnet.cmd`, train on your own to reproduce the submission
⚪ development
- `pip install -r requirements_dev.txt`
- `python preprocess.py -f`
- `python run_sklearn.py` for classical comparations
- `python run_vqnet.py -M ` to train
- see exmaples in `run_vqnet*.cmd`
- `python run_vqnet.py -L ` to eval
#### refenrence
⚪ Q framework & method
- QPanda: [https://qpanda-tutorial.readthedocs.io/zh/latest/](https://qpanda-tutorial.readthedocs.io/zh/latest/)
- PyQPanda: [https://pyqpanda-toturial.readthedocs.io/zh/latest/index.html](https://pyqpanda-toturial.readthedocs.io/zh/latest/index.html)
- VQNet: [https://vqnet20-tutorial.readthedocs.io/en/latest/](https://vqnet20-tutorial.readthedocs.io/en/latest/)
- HEA (Hardware Efficient Ansatz): [https://arxiv.org/abs/1704.05018](https://arxiv.org/abs/1704.05018)
- CQCC (Circuit-Centric Quantum Classifiers): [https://arxiv.org/abs/1804.00633](https://arxiv.org/abs/1804.00633)
- YouroQNet: [https://github.com/Kahsolt/YouroQNet](https://github.com/Kahsolt/YouroQNet)
⚪ problem & data
- kaggle page: [https://www.kaggle.com/competitions/titanic/overview](https://www.kaggle.com/competitions/titanic/overview)
- solution guide: [https://towardsdatascience.com/a-beginners-guide-to-kaggle-s-titanic-problem-3193cb56f6ca](https://towardsdatascience.com/a-beginners-guide-to-kaggle-s-titanic-problem-3193cb56f6ca)
#### Citation
If you find this work useful, please give a star ⭐ and cite~ 😃
```
@misc{kahsolt2023,
author = {Kahsolt},
title = {WheelQNet: Quantum Binary Classification via Rotation Averaging},
howpublished = {\url{https://github.com/Kahsolt/WheelQNet}}
month = {December},
year = {2023}
}
```
----
by Armit
2023/10/27