Science Score: 54.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
-
✓Academic publication links
Links to: arxiv.org -
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (10.5%) to scientific vocabulary
Keywords
Repository
Image Recognition with Quantum LeNet
Basic Info
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Topics
Metadata Files
README.md
Image Recognition with Quantum LeNet
The Quantum LeNet model. The quantum layer consists of embedding, quantum circuits, and measurement.
Quantum computing has shaped our future hope of accomplishing calculations one million times faster than before. Its uses have influenced many things, including machine learning. Such collaboration, known as quantum machine learning (QML), has allowed quantum computers to perform a variety of machine learning tasks. In this project, we will look at how a quantum-based deep-learning model performs image classification on the MNIST dataset. The quantum-based model is a combination of classical and quantum layers. The model is based on LeNet and includes a quantum fully connected layer. The classical and quantum layers are implemented using PyTorch and PennyLane, respectively.
Experiment
Entangle yourself with the implementation using the following link to see the experiment in action.
Result
Quantitative Result
The following table displays the quantitative outcomes.
Test Metric | Score | ----------- | ----- | Accuracy | 96.40% Loss | 0.364
Accuracy and Loss Curves
The model's loss curve on the train and validation sets.
The model's accuracy curve on the train and validation sets.
Qualitative Result
Here, the qualitative results are laid out in the image grid format.
Nine of ten MNIST digits have their corresponding input images along with the predicted and ground-truth labels exposed to view.
Citation
If you think this repository is helpful for your research, you may cite it:
@misc{quantum-lenet,
title = {Image Recognition with Quantum LeNet},
url = {https://github.com/reshalfahsi/quantum-lenet},
author = {Resha Dwika Hefni Al-Fahsi},
}
Credit
Owner
- Name: Resha Dwika Hefni Al-Fahsi
- Login: reshalfahsi
- Kind: user
- Location: Yogyakarta, Indonesia
- Website: reshalfahsi.github.io
- Twitter: reshalfahsi
- Repositories: 80
- Profile: https://github.com/reshalfahsi
Experienced Tensorbender Strolling in the Latent Space
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you think this repository is helpful for your research, you may cite it:"
title: "Image Recognition with Quantum LeNet"
authors:
- family-names: Al-Fahsi
given-names: Resha Dwika Hefni
url: https://github.com/reshalfahsi/quantum-lenet
GitHub Events
Total
- Push event: 6
- Create event: 2
Last Year
- Push event: 6
- Create event: 2
Issues and Pull Requests
Last synced: 11 months ago
All Time
- Total issues: 0
- Total pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 0
- Total pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0