NITeQ/QARBoM.jl
Quantum-Assisted Restricted Boltzmann Machine Framework
This framework helps machine learning researchers train Restricted Boltzmann Machines (RBMs) using both traditional methods and advanced quantum-assisted techniques. You input your dataset and choose a training algorithm, then the framework outputs a trained RBM model capable of learning complex patterns and generating new data. It's designed for quantum computing researchers and advanced machine learning practitioners exploring hybrid classical-quantum models.
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Use this if you are a quantum computing researcher or an advanced machine learning practitioner interested in experimenting with quantum-assisted methods for training Restricted Boltzmann Machines.
Not ideal if you are looking for a simple, off-the-shelf machine learning tool for standard predictive modeling without specific interest in quantum computing.
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Julia
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Last pushed
Aug 09, 2025
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