1 code implementation • 12 Dec 2022 • Katherine Van Kirk, Jordan Cotler, Hsin-Yuan Huang, Mikhail D. Lukin
Efficient characterization of highly entangled multi-particle systems is an outstanding challenge in quantum science.
1 code implementation • 20 Dec 2021 • Cole Miles, Rhine Samajdar, Sepehr Ebadi, Tout T. Wang, Hannes Pichler, Subir Sachdev, Mikhail D. Lukin, Markus Greiner, Kilian Q. Weinberger, Eun-Ah Kim
Specifically, we apply Hybrid-CCNN to analyze new quantum phases on square lattices with programmable interactions.
no code implementations • 20 Jan 2021 • Xun Gao, Eric R. Anschuetz, Sheng-Tao Wang, J. Ignacio Cirac, Mikhail D. Lukin
Generative modeling using samples drawn from the probability distribution constitutes a powerful approach for unsupervised machine learning.
no code implementations • 28 May 2020 • Dominik S. Wild, Dries Sels, Hannes Pichler, Cristian Zanoci, Mikhail D. Lukin
Efficient sampling from a classical Gibbs distribution is an important computational problem with applications ranging from statistical physics over Monte Carlo and optimization algorithms to machine learning.
Quantum Physics Statistical Mechanics
no code implementations • 3 Sep 2019 • Mihir K. Bhaskar, Ralf Riedinger, Bartholomeus Machielse, David S. Levonian, Christian T. Nguyen, Erik N. Knall, Hongkun Park, Dirk Englund, Marko Lončar, Denis D. Sukachev, Mikhail D. Lukin
The ability to communicate quantum information over long distances is of central importance in quantum science and engineering.
Quantum Physics
no code implementations • 16 Aug 2019 • Harry Levine, Alexander Keesling, Giulia Semeghini, Ahmed Omran, Tout T. Wang, Sepehr Ebadi, Hannes Bernien, Markus Greiner, Vladan Vuletić, Hannes Pichler, Mikhail D. Lukin
We report the implementation of universal two- and three-qubit entangling gates on neutral atom qubits encoded in long-lived hyperfine ground states.
Quantum Physics Quantum Gases
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 • 3 Dec 2018 • Leo Zhou, Sheng-Tao Wang, Soonwon Choi, Hannes Pichler, Mikhail D. Lukin
We provide an in-depth study of the performance of QAOA on MaxCut problems by developing an efficient parameter-optimization procedure and revealing its ability to exploit non-adiabatic operations.
Quantum Physics Disordered Systems and Neural Networks Statistical Mechanics
4 code implementations • 9 Oct 2018 • Iris Cong, Soonwon Choi, Mikhail D. Lukin
We introduce and analyze a novel quantum machine learning model motivated by convolutional neural networks (CNN).
Quantum Physics Strongly Correlated Electrons