no code implementations • 28 Aug 2024 • Yi Hong Teoh, Roger G. Melko

Autoregressive models are a class of generative model that probabilistically predict the next output of a sequence based on previous inputs.

1 code implementation • 7 Jun 2024 • Victor Yon, Bastien Galaup, Claude Rohrbacher, Joffrey Rivard, Clément Godfrin, Ruoyu Li, Stefan Kubicek, Kristiaan De Greve, Louis Gaudreau, Eva Dupont-Ferrier, Yann Beilliard, Roger G. Melko, Dominique Drouin

This study presents a machine-learning-based procedure to automate the charge tuning of semiconductor spin qubits with minimal human intervention, addressing one of the significant challenges in scaling up quantum dot technologies.

no code implementations • 5 Dec 2023 • Sehmimul Hoque, Hao Jia, Abhishek Abhishek, Mojde Fadaie, J. Quetzalcoatl Toledo-Marín, Tiago Vale, Roger G. Melko, Maximilian Swiatlowski, Wojciech T. Fedorko

The Large Hadron Collider's high luminosity era presents major computational challenges in the analysis of collision events.

no code implementations • 20 Mar 2023 • Mohamed Hibat-Allah, Roger G. Melko, Juan Carrasquilla

Recurrent neural networks (RNNs), originally developed for natural language processing, hold great promise for accurately describing strongly correlated quantum many-body systems.

1 code implementation • 28 Jul 2022 • Mohamed Hibat-Allah, Roger G. Melko, Juan Carrasquilla

We use symmetry and annealing to obtain accurate estimates of ground state energies of the two-dimensional (2D) Heisenberg model, on the square lattice and on the triangular lattice.

no code implementations • 11 Jun 2021 • Sebastian J. Wetzel, Roger G. Melko, Isaac Tamblyn

Twin neural network regression (TNNR) is a semi-supervised regression algorithm, it can be trained on unlabelled data points as long as other, labelled anchor data points, are present.

2 code implementations • 25 Jan 2021 • Mohamed Hibat-Allah, Estelle M. Inack, Roeland Wiersema, Roger G. Melko, Juan Carrasquilla

Many important challenges in science and technology can be cast as optimization problems.

no code implementations • 29 Dec 2020 • Sebastian J. Wetzel, Kevin Ryczko, Roger G. Melko, Isaac Tamblyn

The solution of a traditional regression problem is then obtained by averaging over an ensemble of all predicted differences between the targets of an unseen data point and all training data points.

no code implementations • 31 Mar 2020 • Isaac J. S. De Vlugt, Dmitri Iouchtchenko, Ejaaz Merali, Pierre-Nicholas Roy, Roger G. Melko

Nanomolecular assemblies of C 60 can be synthesized to enclose dipolar molecules.

Quantum Physics Disordered Systems and Neural Networks Chemical Physics

no code implementations • 9 Mar 2020 • Sebastian J. Wetzel, Roger G. Melko, Joseph Scott, Maysum Panju, Vijay Ganesh

It turns out that in the process of learning which datapoints belong to the same event or field configuration, these SNNs also learn the relevant symmetry invariants and conserved quantities.

2 code implementations • 7 Feb 2020 • Mohamed Hibat-Allah, Martin Ganahl, Lauren E. Hayward, Roger G. Melko, Juan Carrasquilla

A core technology that has emerged from the artificial intelligence revolution is the recurrent neural network (RNN).

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

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 • 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

no code implementations • 15 Aug 2017 • Alan Morningstar, Roger G. Melko

We investigate this question by using unsupervised, generative graphical models to learn the probability distribution of a two-dimensional Ising system.

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.

3 code implementations • 5 May 2016 • Juan Carrasquilla, Roger G. Melko

These results demonstrate the power of machine learning as a basic research tool in the field of condensed matter and statistical physics.

Strongly Correlated Electrons

1 code implementation • 5 Feb 2015 • B. Kulchytskyy, C. M. Herdman, Stephen Inglis, Roger G. Melko

In the face of mounting numerical evidence, Metlitski and Grover [arXiv:1112. 5166] have given compelling analytical arguments that systems with spontaneous broken continuous symmetry contain a sub-leading contribution to the entanglement entropy that diverges logarithmically with system size.

Strongly Correlated Electrons High Energy Physics - Theory Quantum Physics

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