# Introduction

A central task in machine learning applications for quantum many-body systems is certainly
the *learning* part. Loosely speaking, learning refers to a high-dimensional (and typically non-linear) optimization of
the parameters entering a machine, in order to solve a certain task.

NetKet implements learning algorithms to find the ground-state of a given many-body quantum Hamiltonian , see references (1,2). The algorithms to perform this learning task, as implemented in NetKet, are described in the Learning the Ground State section.

In addition to finding the ground-state of a given Hamiltonian, there are other learning tasks that can be performed using the machines implemented in NetKet.
For example, supervised learning with *Born machines* (3,4), or unsupervised learning to perform state-reconstruction (5).

The corresponding learning algorithms will be implemented in future versions of NetKet. See also our Challenges, if you would like to contribute to developments in these directions.

## References

- Carleo, G., & Troyer, M. (2017). Solving the quantum many-body problem with artificial neural networks. Science, 355 602
- Carleo, G. (2017). Lecture notes for the Advanced School on Quantum Science and Quantum technology.
- Stoudenmire, M., & Schwab, D. (2016). Supervised Learning with Quantum-Inspired Tensor Networks. Advances in Neural Information Processing Systems 29, 4799
- Cheng, S., Chen J., & Wang L. (2017). Information Perspective to Probabilistic Modeling: Boltzmann Machines versus Born Machines.
- Torlai, G. et al. (2018). Neural-network quantum state tomography. Nature Physics doi:10.1038/s41567-018-0048-5

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