hdlib 2.0: Extending Machine Learning Capabilities of Vector-Symbolic Architectures

TL;DR

hdlib 2.0 significantly extends the capabilities of Vector-Symbolic Architectures by adding new models for supervised learning, regression, clustering, graph-based learning, and introduces the first quantum hyperdimensional computing implementation.

Contribution

This paper introduces major new features to hdlib, including diverse machine learning models and the first quantum hyperdimensional computing implementation.

Findings

Enhanced supervised classification with feature selection.

New regression, clustering, and graph-based models.

First quantum hyperdimensional computing implementation.

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.