Get Involved - NetKet

Users and developers of NetKet hang out in the following places:
NetKet will not be participating in Google Summer of Code (GSoC) 2022, though we hope to be able to partecipate in the summer of 2023.
If you are interested in contributing to NetKet we would be very happy to welcome you and guide you in the process. Below you can find a list of sensibly-sized projects for which we’d love to find some contributors, such as young PhD students that are starting to be involved in the field.
The projects are written for Google Summer of Code, and if they won’t be tackled by the time being we will offer them again next year. In the meantime, if you are interested in starting to work on them, do get in touch with us!
Even if the Time-Dependent Variational Principle can be used to perform the time-evolution of Neural Quantum States already solving an explicit first order equation. However, in some cases implicit algorithms such as those proposed in ArXiV:1912.08831 might be more stable. This project would see the participant implement functions to optimise the overlap (fidelity) between two states, and use this routine to perform the time evolution according to a first and/or second order implicit equation.
Recommended Skills: Background knowledge in machine learning, numerical analysis and monte carlo methods. Knowledge of Jax.
Expected Results: Implement an implicit-time evolution algorithm
Project Length: 175 hours (can be extended if the candidate wishes to)
Mentors: Filippo Vicentini (@PhilipVinc), Damian Hoffmann (@femtobit)
Tensor Network techniques such as MPS are very established in the numerical community. While for 1-Dimensional systems it is possible to perform computations exactly and there is no need to perform Monte-Carlo sampling, in higher dimensions this is a viable option. This project would see the candidate implementing Tensor Network (MPS and PEPS) functionalities with NetKet, possibly by integrating with existing Jax libraries such as Google’s TensorNetwork.
Recommended Skills: Basic knowledge of tensor network and monte carlo methods. Knowledge of Jax.
Expected Results: Implement MPS with NetKet
Project Length: 175 hours (can be extended to work on PEPS too)
Mentors: Filippo Vicentini (@PhilipVinc)
The Time-Dependent Variational Principle can be used to perform the time-evolution of Neural Quantum States already solving an explicit first order equation. However, in some cases implicit algorithms such as those proposed in ArXiV:1912.08831 might be more stable. This project would see the participant implement functions to optimise the overlap (fidelity) between two states, and use this routine to perform the time evolution according to a first and/or second order implicit equation.
Recommended Skills: Background knowledge in machine learning, numerical analysis and monte carlo methods.
Expected Results: Implement an implicit-time evolution algorithm
Project Length: 175 hours.
Mentors: Filippo Vicentini (@PhilipVinc), Damian Hoffmann (@femtobit)
Right now NetKet provides some high-level objects which cannot be differentiated. We would like to make NetKet fully differentiable with jax.grad. Examples of this are our operators, which can only be indexed with Numba from outside NetKet, or our variational states, which perform some operations outside of a Jax context. The first step in this project would be designing an API to make our operators Jax-friendly while maintaining backward compatibility, and then devising a way to make our high-level API differentiable.
Recommended Skills: Familiarity with Jax and data structure design.
Expected Results: high-level API (expect) must be differentiable.
Project Length: 175 hours.
Mentors: Filippo Vicentini (@PhilipVinc)
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