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Portrait photo of Giacomo Torlai
Portrait photo of Giacomo Torlai
Past

Research Staff

Giacomo Torlai, Ph.D.

Flatiron Research Fellow, Center for Computational Quantum Physics

Affiliations
  • Amazon Quantum Computing (California)

Bio

Giacomo Torlai was a Flatiron Research Fellow at the Center for Computational Quantum Physics from 2018-2020. His research at CCQ focused on the development of novel computational methods based on machine learning algorithms to investigate quantum many-body physics, ranging from strongly-correlated quantum matter to emerging quantum technologies.

Education

Ph.D. Physics, University of Waterloo

M.Sc. Physics, Ludwig Maximilian University Munich

B.Sc. Physics, University of Florence

Selected Publications

”Integrating neural networks with a quantum simulator for state reconstruction”
G. Torlai, B. Timar, E.P.L van Nieuwenburg, H. Levine, A. Omran, A. Keesling, H. Bernien, M. Greiner, V. Vuletić, M.D. Lukin, R.G. Melko and M. Endres
Physical Review Letters 123 , 230504 (2019)

”Neural-network quantum state tomography”
G. Torlai, G. Mazzola, J. Carrasquilla, M. Troyer, R.G. Melko and G. Carleo
Nature Physics 14, 447 (2018)

”Latent space purification via neural density operators”
G. Torlai and R.G. Melko
Physical Review Letters 120, 240503 (2018)

”Neural decoder for topological codes”
G. Torlai and R.G. Melko
Physical Review Letters 119, 030501 (2017)

”Learning thermodynamics with Boltzmann machines”
G. Torlai and R.G. Melko
Physical Review B 94, 165134 (2016)

Publications

Wavefunction positivization via automatic differentiation

Phys. Rev. Research, arXiv:1906.04654, September 2, 2020

Giacomo Torlai, Juan Carrasquilla, Matthew Fishman, Roger G. Melko, and Matthew P. A. Fisher

Precise measurement of quantum observables with neural-network estimators

Physical Review Research, arXiv:1910.07596, June 17, 2020

Giacomo Torlai, Guglielmo Mazzola, Giuseppe Carleo, and Antonio Mezzacapo

Quantum process tomography with unsupervised learning and tensor networks

arXiv:2006.02424, June 3, 2020

Giacomo Torlai, Christopher J. Wood, Atithi Acharya, Giuseppe Carleo, Juan Carrasquilla, and Leandro Aolita

Machine learning quantum states in the NISQ era

Annual Review of Condensed Matter Physics, arXiv:1905.04312, March 2, 2020

Giacomo Torlai and Roger G. Melko

Featuring Giacomo Torlai

Graphic illustrating machine learning. On the left is a box labelled Input Measurements with an arrow to a graphic in the middle labelled Neural Network, which has an arrow pointing to the right labelled Reconstructed Measurements. Input measurements has eight colorful digital illustrations of cats. The Neural Network has three squares and three colorful circles that are all connected. The Reconstructed Measurement boxes has the same colorful cat images in the first box.

Artificial Intelligence Techniques Reconstruct Mysteries of Quantum Systems

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Center for Computational Quantum Physics

CCQ’s mission is to develop the concepts, theories, algorithms and codes needed to solve the quantum many-body problem and use the solutions to predict the behavior of materials and molecules of scientific and technological interest.

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