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20092025
most citedMachine learning and the physical sciences

2.5k citations · 11k across the 97 of their papers we have counts for

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Showing 2019 · quant-phShow all

5 papers · 2 filters

quant-ph2019★ 566 cited

Quantum Simulators: Architectures and Opportunities

Ehud Altman, Kenneth R. Brown, Giuseppe Carleo +34

Quantum simulators are a promising technology on the spectrum of quantum devices from specialized quantum experiments to universal quantum computers. These quantum devices utilize…

quant-ph2019★ 71 cited

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…

quant-ph2019★ 468 cited

Quantum Natural Gradient

James Stokes, Josh Izaac, Nathan Killoran +1

A quantum generalization of Natural Gradient Descent is presented as part of a general-purpose optimization framework for variational quantum circuits. The optimization dynamics is…

quant-ph2019★ 121 cited

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…

quant-ph2019★ 282 cited

Neural-Network Approach to Dissipative Quantum Many-Body Dynamics

Michael J. Hartmann, Giuseppe Carleo

In experimentally realistic situations, quantum systems are never perfectly isolated and the coupling to their environment needs to be taken into account. Often, the effect of the…