qkan and LinearKAN
One is a PyTorch implementation of a "Quantum-inspired Kolmogorov-Arnold Network," while the other is a very fast implementation of a standard "Kolmogorov-Arnold Network," making them **competitors** offering different specialized approaches within the same overarching category.
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 LinearKAN
catswe/LinearKAN
LinearKAN: A very fast implementation of Kolmogorov-Arnold Networks
This project offers a significantly faster way to train and use Kolmogorov-Arnold Networks (KANs) for machine learning tasks. It takes structured numerical data as input and produces processed numerical outputs, helping machine learning practitioners build and deploy models more efficiently. If you're working with AI models and need a powerful, yet accelerated alternative to traditional neural networks, this tool can help.
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