G
Giuseppe Carleo
Researcher at École Polytechnique Fédérale de Lausanne
Publications - 103
Citations - 8617
Giuseppe Carleo is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Quantum state & Quantum. The author has an hindex of 31, co-authored 76 publications receiving 5527 citations. Previous affiliations of Giuseppe Carleo include Centre national de la recherche scientifique & International School for Advanced Studies.
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Journal ArticleDOI
Machine learning and the physical sciences
Giuseppe Carleo,J. Ignacio Cirac,Kyle Cranmer,Laurent Daudet,Maria Schuld,Naftali Tishby,Leslie Vogt-Maranto,Lenka Zdeborová +7 more
TL;DR: This article reviews in a selective way the recent research on the interface between machine learning and the physical sciences, including conceptual developments in ML motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross fertilization between the two fields.
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Solving the quantum many-body problem with artificial neural networks
TL;DR: In this paper, a variational representation of quantum states based on artificial neural networks with a variable number of hidden neurons is introduced. But this model is not suitable for the many-body problem in quantum physics.
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Solving the Quantum Many-Body Problem with Artificial Neural Networks
TL;DR: A variational representation of quantum states based on artificial neural networks with a variable number of hidden neurons and a reinforcement-learning scheme that is capable of both finding the ground state and describing the unitary time evolution of complex interacting quantum systems.
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Neural-network quantum state tomography
Giacomo Torlai,Giacomo Torlai,Guglielmo Mazzola,Juan Carrasquilla,Matthias Troyer,Matthias Troyer,Roger G. Melko,Roger G. Melko,Giuseppe Carleo +8 more
TL;DR: It is demonstrated that machine learning allows one to reconstruct traditionally challenging many-body quantities—such as the entanglement entropy—from simple, experimentally accessible measurements, and can benefit existing and future generations of devices.
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Many-body quantum state tomography with neural networks
Giacomo Torlai,Guglielmo Mazzola,Juan Carrasquilla,Matthias Troyer,Roger G. Melko,Giuseppe Carleo +5 more
TL;DR: In this paper, machine learning techniques are used for quantum state tomography (QST) of highly entangled states, in both one and two dimensions, and the resulting approach allows one to reconstruct traditionally challenging many-body quantities - such as the entanglement entropy - from simple, experimentally accessible measurements.