1 code implementation • 30 May 2024 • Mohamed Hibat-Allah, Ejaaz Merali, Giacomo Torlai, Roger G Melko, Juan Carrasquilla

Rydberg atom array experiments have demonstrated the ability to act as powerful quantum simulators, preparing strongly-correlated phases of matter which are challenging to study for conventional computer simulations.

2 code implementations • 19 Jan 2023 • Juan Carrasquilla, Mohamed Hibat-Allah, Estelle Inack, Alireza Makhzani, Kirill Neklyudov, Graham W. Taylor, Giacomo Torlai

Binary neural networks, i. e., neural networks whose parameters and activations are constrained to only two possible values, offer a compelling avenue for the deployment of deep learning models on energy- and memory-limited devices.

3 code implementations • 23 Jun 2021 • Hsin-Yuan Huang, Richard Kueng, Giacomo Torlai, Victor V. Albert, John Preskill

In this work, we prove that classical ML algorithms can efficiently predict ground state properties of gapped Hamiltonians in finite spatial dimensions, after learning from data obtained by measuring other Hamiltonians in the same quantum phase of matter.

no code implementations • 16 Oct 2019 • Giacomo Torlai, Guglielmo Mazzola, Giuseppe Carleo, Antonio Mezzacapo

The measurement precision of modern quantum simulators is intrinsically constrained by the limited set of measurements that can be efficiently implemented on hardware.

Quantum Physics Disordered Systems and Neural Networks Strongly Correlated Electrons

no code implementations • 10 May 2019 • 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.

Quantum Physics Disordered Systems and Neural Networks Quantum Gases Strongly Correlated Electrons

no code implementations • 17 Apr 2019 • Giacomo Torlai, Brian Timar, Evert P. L. van Nieuwenburg, Harry Levine, Ahmed Omran, Alexander Keesling, Hannes Bernien, Markus Greiner, Vladan Vuletić, Mikhail D. Lukin, Roger G. Melko, Manuel Endres

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 experimental errors.

Quantum Physics Quantum Gases

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

1 code implementation • 21 Dec 2018 • Matthew J. S. Beach, Isaac De Vlugt, Anna Golubeva, Patrick Huembeli, Bohdan Kulchytskyy, Xiuzhe Luo, Roger G. Melko, Ejaaz Merali, Giacomo Torlai

As we enter a new era of quantum technology, it is increasingly important to develop methods to aid in the accurate preparation of quantum states for a variety of materials, matter, and devices.

Quantum Physics Strongly Correlated Electrons

1 code implementation • 8 Jun 2016 • Giacomo Torlai, Roger G. Melko

A Boltzmann machine is a stochastic neural network that has been extensively used in the layers of deep architectures for modern machine learning applications.

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