{"id":21842558,"url":"https://github.com/reshalfahsi/quantum-lenet","last_synced_at":"2026-05-21T07:02:37.709Z","repository":{"id":264021202,"uuid":"892112381","full_name":"reshalfahsi/quantum-lenet","owner":"reshalfahsi","description":"Image Recognition with Quantum LeNet","archived":false,"fork":false,"pushed_at":"2024-11-23T05:40:49.000Z","size":953,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2026-03-28T12:45:19.747Z","etag":null,"topics":["image-classification","image-recognition","lenet","mnist","pennylane","pytorch","pytorch-lightning","quantum-computing","quantum-lenet","quantum-machine-learning"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/reshalfahsi.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2024-11-21T14:30:42.000Z","updated_at":"2024-11-25T02:04:32.000Z","dependencies_parsed_at":"2025-01-26T11:34:22.025Z","dependency_job_id":null,"html_url":"https://github.com/reshalfahsi/quantum-lenet","commit_stats":null,"previous_names":["reshalfahsi/quantum-lenet"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/reshalfahsi/quantum-lenet","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/reshalfahsi%2Fquantum-lenet","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/reshalfahsi%2Fquantum-lenet/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/reshalfahsi%2Fquantum-lenet/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/reshalfahsi%2Fquantum-lenet/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/reshalfahsi","download_url":"https://codeload.github.com/reshalfahsi/quantum-lenet/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/reshalfahsi%2Fquantum-lenet/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33292071,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-21T02:57:32.698Z","status":"ssl_error","status_checked_at":"2026-05-21T02:57:31.990Z","response_time":62,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["image-classification","image-recognition","lenet","mnist","pennylane","pytorch","pytorch-lightning","quantum-computing","quantum-lenet","quantum-machine-learning"],"created_at":"2024-11-27T22:12:29.750Z","updated_at":"2026-05-21T07:02:37.678Z","avatar_url":"https://github.com/reshalfahsi.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Image Recognition with Quantum LeNet\n\n\n\u003cdiv align=\"center\"\u003e\n    \u003ca href=\"https://colab.research.google.com/github/reshalfahsi/quantum-lenet/blob/master/Image_Recognition_with_Quantum_LeNet.ipynb\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"colab\"\u003e\u003c/a\u003e\n    \u003cbr /\u003e\n\u003c/div\u003e\n\n\n\u003cp align=\"center\"\u003e \u003cimg src=\"https://github.com/reshalfahsi/quantum-lenet/blob/master/assets/quantum-lenet.png\" alt=\"quantum-lenet\" \u003e \u003cbr /\u003e The Quantum LeNet model. The quantum layer consists of embedding, quantum circuits, and measurement. \u003c/p\u003e\n\n\nQuantum 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.\n\n\n## Experiment\n\nEntangle yourself with the implementation using the following [link](https://github.com/reshalfahsi/quantum-lenet/blob/master/Image_Recognition_with_Quantum_LeNet.ipynb) to see the experiment in action.\n\n\n## Result\n\n## Quantitative Result\n\nThe following table displays the quantitative outcomes.\n\nTest Metric | Score |\n----------- | ----- |\nAccuracy | 96.40%\nLoss | 0.364\n\n\n## Accuracy and Loss Curves\n\n\u003cp align=\"center\"\u003e \u003cimg src=\"https://github.com/reshalfahsi/quantum-lenet/blob/master/assets/loss_curve.png\" alt=\"loss_curve\" \u003e \u003cbr /\u003e The model's loss curve on the train and validation sets. \u003c/p\u003e\n\n\u003cp align=\"center\"\u003e \u003cimg src=\"https://github.com/reshalfahsi/quantum-lenet/blob/master/assets/acc_curve.png\" alt=\"acc_curve\" \u003e \u003cbr /\u003e The model's accuracy curve on the train and validation sets. \u003c/p\u003e\n\n\n## Qualitative Result\n\nHere, the qualitative results are laid out in the image grid format.\n\n\u003cp align=\"center\"\u003e \u003cimg src=\"https://github.com/reshalfahsi/quantum-lenet/blob/master/assets/qualitative.png\" alt=\"qualitative\" \u003e \u003cbr /\u003e Nine of ten MNIST digits have their corresponding input images along with the predicted and ground-truth labels exposed to view. \u003c/p\u003e\n\n\n## Citation\n\nIf you think this repository is helpful for your research, you may cite it:\n\n```\n@misc{quantum-lenet,\n   title = {Image Recognition with Quantum LeNet},\n   url = {https://github.com/reshalfahsi/quantum-lenet},\n   author = {Resha Dwika Hefni Al-Fahsi},\n}\n```\n\n\n\n## Credit\n\n- [Gradient-Based Learning Applied to Document Recognition](http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf)\n- [PennyLane: Automatic differentiation of hybrid quantum-classical computations](https://arxiv.org/pdf/1811.04968)\n- [Turning quantum nodes into Torch Layers](https://pennylane.ai/qml/demos/tutorial_qnn_module_torch)\n- [PyTorch Lightning](https://lightning.ai/docs/pytorch/latest/)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Freshalfahsi%2Fquantum-lenet","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Freshalfahsi%2Fquantum-lenet","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Freshalfahsi%2Fquantum-lenet/lists"}