Distributive Pre-Training of Generative Modeling Using Matrix-Product States

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.

Density Estimation Tensor Networks

Scaling of neural-network quantum states for time evolution

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.

Real- and imaginary-time evolution with compressed quantum circuits

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

NetKet: A Machine Learning Toolkit for Many-Body Quantum Systems

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