Adaptively tuned overdispersed importance sampling, q_alpha proportional to |psi|^alpha, cuts the Monte Carlo sample count needed to converge neural quantum states, especially for peaked molecular wavefunctions.
NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems
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abstract
We introduce version 3 of NetKet, the machine learning toolbox for many-body quantum physics. NetKet is built around neural-network quantum states and provides efficient algorithms for their evaluation and optimization. This new version is built on top of JAX, a differentiable programming and accelerated linear algebra framework for the Python programming language. The most significant new feature is the possibility to define arbitrary neural network ans\"atze 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. 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 for their own code.
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quant-ph 1years
2025 1verdicts
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Looking elsewhere: improving variational Monte Carlo gradients by importance sampling
Adaptively tuned overdispersed importance sampling, q_alpha proportional to |psi|^alpha, cuts the Monte Carlo sample count needed to converge neural quantum states, especially for peaked molecular wavefunctions.