many body physics
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scholarly journals How To Use Neural Networks To Investigate Quantum Many-Body Physics

PRX Quantum ◽  
2021 ◽  
Vol 2 (4) ◽  
Author(s):  
Juan Carrasquilla ◽  
Giacomo Torlai
Keyword(s):  
Neural Networks ◽  
Many Body ◽  
Many Body Physics

scholarly journals Many-Body Physics in the NISQ Era: Quantum Programming a Discrete Time Crystal

PRX Quantum ◽  
2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Matteo Ippoliti ◽  
Kostyantyn Kechedzhi ◽  
Roderich Moessner ◽  
S.L. Sondhi ◽  
Vedika Khemani
Keyword(s):  
Discrete Time ◽  
Many Body ◽  
Quantum Programming ◽  
Many Body Physics

scholarly journals Image Denoising Inspired by Quantum Many-Body physics

2021 ◽  
Author(s):  
Sayantan Dutta ◽  
Adrian Basarab ◽  
Bertrand Georgeot ◽  
Denis Kouame
Keyword(s):  
Image Denoising ◽  
Many Body ◽  
Many Body Physics

Despeckling Ultrasound Images Using Quantum Many-Body Physics

2021 ◽  
Author(s):  
Sayantan Dutta ◽  
Adrian Basarab ◽  
Bertrand Georgeot ◽  
Denis Kouame
Keyword(s):  
Ultrasound Images ◽  
Many Body ◽  
Many Body Physics

scholarly journals Many-body physics in small systems: Observing the onset and saturation of correlation in linear atomic chains

2021 ◽  
Vol 103 (19) ◽  
Author(s):  
Emily Townsend ◽  
Tomáš Neuman ◽  
Alex Debrecht ◽  
Javier Aizpurua ◽  
Garnett W. Bryant
Keyword(s):  
Many Body ◽  
Small Systems ◽  
Atomic Chains ◽  
Many Body Physics

scholarly journals Cavity QED with quantum gases: new paradigms in many-body physics

2021 ◽  
Vol 70 (1) ◽  
pp. 1-153
Author(s):  
Farokh Mivehvar ◽  
Francesco Piazza ◽  
Tobias Donner ◽  
Helmut Ritsch
Keyword(s):  
Cavity Qed ◽  
Quantum Gases ◽  
Many Body ◽  
Many Body Physics

Random Numbers Can Help Solve Difficult Problems in Many-body Physics

2021 ◽  
Vol 1 ◽  
Keyword(s):  
Quantum Computing ◽  
Numerical Simulations ◽  
State Vector ◽  
Random Numbers ◽  
Many Body ◽  
Computational Burden ◽  
Many Body Systems ◽  
Random State ◽  
Many Body Physics

Theorists review a random state vector-based description of quantum many-body systems which helps greatly reduce the computational burden involved in their numerical simulations, opening doors to applications in quantum computing.

scholarly journals Gaussian Process States: A Data-Driven Representation of Quantum Many-Body Physics

2020 ◽  
Vol 10 (4) ◽  
Author(s):  
Aldo Glielmo ◽  
Yannic Rath ◽  
Gábor Csányi ◽  
Alessandro De Vita ◽  
George H. Booth
Keyword(s):  
Gaussian Process ◽  
Data Driven ◽  
Many Body ◽  
Many Body Physics
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