- Journal Article
Rights / licenseCreative Commons Attribution 4.0 International
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. Show more
Journal / seriesSoftwareX
Pages / Article No.
SubjectNeural-network quantum states; Variational Monte Carlo; Quantum state tomography; Machine learning; Supervised learning
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