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NetKet: A machine learning toolkit for many-body quantum systems

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Hofmann,  D.
Theoretical Description of Pump-Probe Spectroscopies in Solids, Theory Department, Max Planck Institute for the Structure and Dynamics of Matter, Max Planck Society;

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1-s2.0-S2352711019300974-main.pdf
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Citation

Carleo, G., Choo, K., Hofmann, D., Smith, J. E. T., Westerhout, T., Alet, F., et al. (2019). NetKet: A machine learning toolkit for many-body quantum systems. SoftwareX, 10: 100311. doi:10.1016/j.softx.2019.100311.


Cite as: http://hdl.handle.net/21.11116/0000-0004-ADDD-1
Abstract
We introduce NetKet, a comprehensive open source framework for the study of many-body quantum systems using machine learning techniques. The framework is built around a general and flexible implementation of neural-network quantum states, which are used as a variational ansatz for quantum wavefunctions. NetKet provides algorithms for several key tasks in quantum many-body physics and quantum technology, namely quantum state tomography, supervised learning from wavefunction data, and ground state searches for a wide range of customizable lattice models. Our aim is to provide a common platform for open research and to stimulate the collaborative development of computational methods at the interface of machine learning and many-body physics.