{"ID":2822709,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.02509","arxiv_id":"2601.02509","title":"hdlib 2.0: Extending Machine Learning Capabilities of Vector-Symbolic Architectures","abstract":"Following the initial publication of hdlib, a Python library for designing Vector-Symbolic Architectures (VSA), we introduce a major extension that significantly enhances its machine learning capabilities. VSA, also known as Hyperdimensional Computing, is a computing paradigm that represents and processes information using high-dimensional vectors. While the first version of hdlib established a robust foundation for creating and manipulating these vectors, this update addresses the growing need for more advanced, data-driven modeling within the VSA framework. Here, we present four extensions: significant enhancements to the existing supervised classification model also enabling feature selection, and a new regression model for predicting continuous variables, a clustering model for unsupervised learning, and a graph-based learning model. Furthermore, we propose the first implementation ever of Quantum Hyperdimensional Computing with quantum-powered arithmetic operations and a new Quantum Machine Learning model for supervised learning. hdlib remains open-source and available on GitHub at https://github.com/cumbof/hdlib under the MIT license, and distributed through the Python Package Index (pip install hdlib) and Conda (conda install -c conda-forge hdlib). Documentation and examples of these new features are available on the official Wiki at https://github.com/cumbof/hdlib/wiki.","short_abstract":"Following the initial publication of hdlib, a Python library for designing Vector-Symbolic Architectures (VSA), we introduce a major extension that significantly enhances its machine learning capabilities. VSA, also known as Hyperdimensional Computing, is a computing paradigm that represents and processes information u...","url_abs":"https://arxiv.org/abs/2601.02509","url_pdf":"https://arxiv.org/pdf/2601.02509v1","authors":"[\"Fabio Cumbo\",\"Kabir Dhillon\",\"Daniel Blankenberg\"]","published":"2026-01-05T19:25:42Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false,"code_links":[{"ID":605439,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2822709,"paper_url":"https://arxiv.org/abs/2601.02509","paper_title":"hdlib 2.0: Extending Machine Learning Capabilities of Vector-Symbolic Architectures","repo_url":"https://github.com/cumbof/hdlib","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}