gerardPlanella/QGNN
UvA Master thesis "Towards Quantum Graph Neural Networks". The research explores integrating quantum physics into Graph Neural Networks through methods like Tensor Networks to tackle computational challenges in quantum many-body systems.
This project offers tools to simulate and analyze quantum many-body systems, which are notoriously difficult to model due to their immense complexity. It takes in descriptions of quantum systems, such as Matrix Product States or Projected Entangled Pair States, and outputs insights into their behavior, potentially offering more scalable solutions than traditional methods. A quantum physicist or researcher working with complex quantum systems would find this useful.
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Use this if you are a quantum physicist or researcher struggling with the computational demands of modeling complex quantum many-body systems and seeking alternative, scalable approaches.
Not ideal if you need established, highly optimized classical tensor network methods for smaller systems or if you are not familiar with quantum physics concepts and graph neural networks.
Stars
8
Forks
1
Language
Python
License
MIT
Category
Last pushed
Aug 22, 2024
Commits (30d)
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