825 citations · 2.8k across the 22 of their papers we have counts for
6 papers · 1 filter
Precise measurement of quantum observables with neural-network estimators
Giacomo Torlai, Guglielmo Mazzola, Giuseppe Carleo +1
The measurement precision of modern quantum simulators is intrinsically constrained by the limited set of measurements that can be efficiently implemented on hardware. This fundame…
The learnability scaling of quantum states: restricted Boltzmann machines
Dan Sehayek, Anna Golubeva, Michael S. Albergo +3
Generative modeling with machine learning has provided a new perspective on the data-driven task of reconstructing quantum states from a set of qubit measurements. As increasingly…
Wavefunction positivization via automatic differentiation
Giacomo Torlai, Juan Carrasquilla, Matthew T. Fishman +2
We introduce a procedure to systematically search for a local unitary transformation that maps a wavefunction with a non-trivial sign structure into a positive-real form. The trans…
Machine learning quantum states in the NISQ era
Giacomo Torlai, Roger G. Melko
We review the development of generative modeling techniques in machine learning for the purpose of reconstructing real, noisy, many-qubit quantum states. Motivated by its interpret…
Integrating Neural Networks with a Quantum Simulator for State Reconstruction
Giacomo Torlai, Brian Timar, Evert P. L. van Nieuwenburg +9
We demonstrate quantum many-body state reconstruction from experimental data generated by a programmable quantum simulator, by means of a neural network model incorporating known e…
NetKet: A Machine Learning Toolkit for Many-Body Quantum Systems
Giuseppe Carleo, Kenny Choo, Damian Hofmann +15
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…