Create proposal_Baler-ProbabilisticCircuit.md (#1687)

Author: neogyk

_gsocproposals/2025/proposal_Baler-ProbabilisticCircuit.md

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+---
+title: Probabilistic circuit for lossless HEP data compression
+layout: gsoc_proposal
+project: Baler
+year: 2025
+organization:
+  - CERN
+difficulty: medium
+duration: 350
+mentor_avail: June-October (with 3 weeks mentor vacation where student will work independently with minimal guidance)
+---
+​
+## Short description of the project
+Neural data compression is an efficient solution for reducing the cost and computational resources of data storage in many LHC experiments.
+However, it suffers from the ability to precisely reconstruct compressed data, as most of the neural compression algorithms perform the decompression with the information loosage.
+On another hand, the lossless neural data compression schemas (VAE, IDF) have a lower compression ratio and are not fast enough for file IO.
+This project's task is to overcome the disadvantages of the neural compression algorithm by using the probabilistic circuit for HEP data compression.
+
+## Task ideas
+
+* Implement the probabilistic circuit using the PyTorch
+* Train and compress the HEP data (Higgs data, TopQuark Dataset)
+* Measure the cost and quantify the optimal compression ratio of the probabilistic circuit
+* Perform the benchmark, and compare the results with AE, Transformer
+
+## Expected results
+
+An improved compression performance with documentation and figures of merit that may include:
+  * Implemented model of the probabilistic circuit
+  * Documentation of the benchmark and experiment of compression of the HEP data
+
+## Requirements
+
+Required: Good knowledge of UNIX, Python, matplotlib, Pytorch, Julia, Pandas, ROOT. 
+
+## Mentors
+   * ***[Leonid Didukh](mailto:[email protected])***
+   * [Caterina Doglioni](mailto:[email protected]) as backup mentor
+
+## Links
+
+* Previous work:
+  
+   * [GSOC 2021 project: Zenodo entry by George Dialektakis](https://zenodo.org/record/5482611#.Y-I28S2l3fa)
+   * [Baler -- Machine Learning Based Compression of Scientific Data
+](https://arxiv.org/abs/2305.02283)
+
+ * [ROOT](https://root.cern/)
+ * [Jupyter](http://jupyter.org)
+ * [Lossless compression with probabilistic circuits](https://arxiv.org/pdf/2111.11632)
+ * [iFlow: Numerically Invertible Flows for Efficient Lossless Compression via a Uniform Coder](https://arxiv.org/pdf/2111.00965)
+ * [Integer Discrete Flows and Lossless Compression](https://arxiv.org/pdf/1905.07376)
+   
+
+