🧰 NetKet

• $75 | Add support for a single-file HDF5 log
• $50 | Use colors to signal sampling issues in expectation values
• $100 | Support ladder operators in PauliStrings Operators
• $75 | Implement single-pass statistics estimators for variance and convergence estimators

NetKet uses Machine-Learning ideas to address some hard problems in Quantum Physics, such as finding the ground-state or solving the dynamics of a quantum system, or enhancing a tomographic reconstruction from some experimental data. It is built around neural-network quantum states and provides efficient algorithms for their evaluation and optimization. NetKet is a Python package based on Google's Jax library.

If you are not familiar with NetKet, you can learn more by looking at our documentation or by inspecting this tutorial.

We are welcoming contributors with a Physics or Machine Learning background to work on some algorithmic tasks, those with a quantum computing background to work on our integration with other packages, and those without to work on the structure of the codebase itself.

Open NetKet GitHub issues are here.