qkan and KAN-PyTorch
These are competitors offering alternative PyTorch implementations of Kolmogorov-Arnold Networks, with A extending the concept to quantum-inspired variants while B provides a foundational KAN implementation, so users would typically choose one based on whether they need quantum inspiration or classical KAN architecture.
About qkan
Jim137/qkan
PyTorch implementation of QKAN "Quantum-inspired Kolmogorov-Arnold Network" https://arxiv.org/abs/2509.14026
This tool helps researchers and machine learning practitioners build and train "Quantum-inspired Kolmogorov-Arnold Networks" (QKANs). It takes raw data and model configuration to produce trained models that can fit functions, classify data, or generate new data. This is ideal for those exploring advanced neural network architectures, especially those interested in quantum computing's influence on AI.
About KAN-PyTorch
Simon-Bertrand/KAN-PyTorch
Kolmogorov–Arnold Networks (KAN) in PyTorch
This is a specialized tool for machine learning researchers and practitioners who are experimenting with novel neural network architectures. It allows you to build models using Kolmogorov-Arnold Networks (KANs), which are an alternative to traditional Multi-Layer Perceptrons (MLPs). You input your dataset and get a KAN-based model that can be trained for tasks like classification.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work