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Status: Unchecked ×Keyword: discrete symmetry groups ×Clear all

2 claims from 1 paper

1. Physics and Astronomy › Quantum many-body systems

   NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems

   Vicentini, Hofmann, Szabó et al. · SciPost Physics Codebases · 2022

   The paper presents version 3 of NetKet, a toolbox for many-body quantum physics built around neural-network quantum states and rebuilt on JAX, with new features for scale, symmetry and dynamics.

   Unchecked2 claims
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   1. UncheckedNetKet 3's main new feature lets users write their own neural network models in plain Python, with compilation and gradients handled automatically.“The most significant new feature is the possibility to define arbitrary neural network ansätze in pure Python code using the concise notation of machine-learning frameworks, which allows for just-in-time compilation as well as the implicit generation of gradients thanks to automatic differentiation.”
   2. UncheckedNetKet 3 adds GPU and TPU support, symmetry handling, chunking for large systems, quantum dynamics drivers and a more modular design.“NetKet 3 also comes with support for GPU and TPU accelerators, advanced support for discrete symmetry groups, chunking to scale up to thousands of degrees of freedom, drivers for quantum dynamics applications, and improved modularity, allowing users to use only parts of the toolbox as a foundation…”