{"id":39087396,"url":"https://github.com/vbelis/latent-ad-qml","last_synced_at":"2026-01-17T18:42:33.790Z","repository":{"id":117490880,"uuid":"494404586","full_name":"vbelis/latent-ad-qml","owner":"vbelis","description":"Unsupervised anomaly detection in the latent space of high energy physics events with quantum machine learning. 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Quantum anomaly detection in the latent space of proton collision events at the LHC. _Nature Communications Physics_ **7**, 334 (2024). https://doi.org/10.1038/s42005-024-01811-6\n\nIn this work, we investigate unsupervised quantum machine learning algorithms for anomaly detection tasks in particle physics data. \nThe `qad` package associated with this work was created for reproducibility of the results and ease-of-use in future studies.\n\u003cp align=\"center\"\u003e\n\u003cimg src=\"https://github.com/vbelis/latent-ad-qml/blob/docs-reformat/docs/Pipeline_QML.png?raw=true\" alt=\"Sublime's custom image\"/\u003e\n\u003c/p\u003e\n\nThe figure above, taken from the paper, depicts the _quantum\\-classical pipeline_ for detecting (anomalous) new-physics events in proton collisions at the LHC. Our strategy, implemented in `qad`, combines a data compression scheme with unsupervised quantum machine learning models to assist in scientific discovery at high energy physics experiments.\n\n## Documentation \nThe documentation can be consulted in the readthedocs [page](https://latent-ad-qml.readthedocs.io/en/latest/).\n\n## Citation\nPlease cite our work if you found it useful in your own research.\n```\n @article{Belis_2024, \n    title={Quantum anomaly detection in the latent space of proton collision events at the LHC},\n    volume={7},\n    ISSN={2399-3650},\n    DOI={10.1038/s42005-024-01811-6},\n    number={1},\n    journal={Communications Physics}, \n    author={Belis, Vasilis and Woźniak, Kinga Anna and Puljak, Ema and Barkoutsos, Panagiotis and Dissertori, Günther and Grossi, Michele and Pierini, Maurizio and Reiter, Florentin and Tavernelli, Ivano and Vallecorsa, Sofia}, year={2024}, \n    month=oct, \n    pages={334} \n}\n```\n\n\n\n## How to install\nThe package can be installed with Python's `pip` package manager. We recommend installing the dependencies and the package within a dedicated environment. For instance, we use `conda` to create a `python` environment:\n```\nconda create -n my_env python=3.8\nconda activate my_env\n```\nIn this environment you can directly install `qad` by running:\n\n```\npip install https://github.com/vbelis/latent-ad-qml/archive/main.zip\n```\nor by first cloning the repo locally and then installing the package:\n```bash\ngit clone https://github.com/vbelis/latent-ad-qml.git\ncd latent-ad-qml\npip install .\n```\nThe installation is expected to take a couple of minutes.\n\n## Usage\nExamples on how to run the code and use `qad` to reproduce results and plots from the paper can be found in the [scripts](https://github.com/vbelis/latent-ad-qml/tree/main/scripts). Check also the corresponding documentation page.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvbelis%2Flatent-ad-qml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvbelis%2Flatent-ad-qml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvbelis%2Flatent-ad-qml/lists"}