no code implementations • 26 Jun 2023 • Sheng-Hsuan Lin, Olivier Kuijpers, Sebastian Peterhansl, Frank Pollmann
Tensor networks have recently found applications in machine learning for both supervised learning and unsupervised learning.
1 code implementation • 21 Apr 2021 • Sheng-Hsuan Lin, Frank Pollmann
Simulating quantum many-body dynamics on classical computers is a challenging problem due to the exponential growth of the Hilbert space.
no code implementations • 24 Aug 2020 • Sheng-Hsuan Lin, Rohit Dilip, Andrew G. Green, Adam Smith, Frank Pollmann
The current generation of noisy intermediate scale quantum computers introduces new opportunities to study quantum many-body systems.
Quantum Physics Mesoscale and Nanoscale Physics Strongly Correlated Electrons
1 code implementation • 29 Mar 2019 • Giuseppe Carleo, Kenny Choo, Damian Hofmann, James E. T. Smith, Tom Westerhout, Fabien Alet, Emily J. Davis, Stavros Efthymiou, Ivan Glasser, Sheng-Hsuan Lin, Marta Mauri, Guglielmo Mazzola, Christian B. Mendl, Evert van Nieuwenburg, Ossian O'Reilly, Hugo Théveniaut, Giacomo Torlai, Alexander Wietek
We introduce NetKet, a comprehensive open source framework for the study of many-body quantum systems using machine learning techniques.
Quantum Physics Disordered Systems and Neural Networks Strongly Correlated Electrons Computational Physics Data Analysis, Statistics and Probability