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

Carleo, G.; Choo, K.; Hofmann, D.; Smith, J. E. T.; Westerhout, T.; Alet, Fabien; Davis, E. J.; Efthymiou, S.; Glasser, I.; Lin, S.-H.; Mauri, M.; Mazzola, G.; Mendl, C. B.; van Nieuwenburg, E.; O’Reilly, O.; Théveniaut, H.; Torlai, G.; Vicentini, F.; Wietek, A.

doi:10.1016/j.softx.2019.100311

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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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https://dx.doi.org/10.1016/j.softx.2019.100311
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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.

Zitierlink: https://hdl.handle.net/21.11116/0000-0004-ADDD-1

Zusammenfassung

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.