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.
1 code implementation • 6 Nov 2020 • Cole Miles, Annabelle Bohrdt, Ruihan Wu, Christie Chiu, Muqing Xu, Geoffrey Ji, Markus Greiner, Kilian Q. Weinberger, Eugene Demler, Eun-Ah Kim
Machine learning models are a powerful theoretical tool for analyzing data from quantum simulators, in which results of experiments are sets of snapshots of many-body states.
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 • 24 May 2018 • Alexander Lukin, Matthew Rispoli, Robert Schittko, M. Eric Tai, Adam M. Kaufman, Soonwon Choi, Vedika Khemani, Julian Léonard, Markus Greiner
The key to our understanding of this phenomenon lies in the system's entanglement, which is experimentally challenging to measure.
Quantum Gases Disordered Systems and Neural Networks Statistical Mechanics Atomic Physics