Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Cole Miles"'
Autor:
Cole Miles, Annabelle Bohrdt, Ruihan Wu, Christie Chiu, Muqing Xu, Geoffrey Ji, Markus Greiner, Kilian Q. Weinberger, Eugene Demler, Eun-Ah Kim
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-7 (2021)
Physical principles underlying machine learning analysis of quantum gas microscopy data are not well understood. Here the authors develop a neural network based approach to classify image data in terms of multi-site correlation functions and reveal t
Externí odkaz:
https://doaj.org/article/c5f988f964a14deb9630bb1251319555
Autor:
Cole Miles, Rhine Samajdar, Sepehr Ebadi, Tout T. Wang, Hannes Pichler, Subir Sachdev, Mikhail D. Lukin, Markus Greiner, Kilian Q. Weinberger, Eun-Ah Kim
Publikováno v:
Physical Review Research, Vol 5, Iss 1, p 013026 (2023)
Machine learning has recently emerged as a promising approach for studying complex phenomena characterized by rich datasets. In particular, data-centric approaches lead to the possibility of automatically discovering structures in experimental datase
Externí odkaz:
https://doaj.org/article/db2b65c03afd43028948bc00e8dd95b5
Classical models of spin systems traditionally retain only the dipole moments, but a quantum spin state will frequently have additional structure. Spins of magnitude $S$ have $N=2S+1$ levels. Alternatively, the spin state is fully characterized by a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d9b44f8452f990b92fc056a107ff935
http://arxiv.org/abs/2209.01265
http://arxiv.org/abs/2209.01265
Autor:
Benjamin Cohen-Stead, Owen Bradley, Cole Miles, George Batrouni, Richard Scalettar, Kipton Barros
We introduce methodologies for highly scalable quantum Monte Carlo simulations of electron-phonon models, and we report benchmark results for the Holstein model on the square lattice. The determinant quantum Monte Carlo (DQMC) method is a widely used
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0bb975bc8b91bdf94b7789170316ae5d
http://arxiv.org/abs/2203.01291
http://arxiv.org/abs/2203.01291
Autor:
Cole Miles, Benjamin Cohen-Stead, Owen Bradley, Steven Johnston, Richard Scalettar, Kipton Barros
We present a method to facilitate Monte Carlo simulations in the grand canonical ensemble given a target mean particle number. The method imposes a fictitious dynamics on the chemical potential, to be run concurrently with the Monte Carlo sampling of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::29175ee8ffc6718044bed7220bb379a1
http://arxiv.org/abs/2201.01296
http://arxiv.org/abs/2201.01296
The Landau-Lifshitz equation describes the time-evolution of magnetic dipoles, and can be derived by taking the classical limit of a quantum mechanical spin Hamiltonian. To take this limit, one constrains the many-body quantum state to a tensor produ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e86deec6f599448f9b3abe74b168a237
Autor:
Cole Miles, Rhine Samajdar, Sepehr Ebadi, Tout T. Wang, Hannes Pichler, Subir Sachdev, Mikhail D. Lukin, Markus Greiner, Kilian Q. Weinberger, Eun-Ah Kim
Publikováno v:
Eun-Ah Kim
Machine learning has recently emerged as a promising approach for studying complex phenomena characterized by rich datasets. In particular, data-centric approaches lend to the possibility of automatically discovering structures in experimental datase
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b26f3a642a00c33bdd179b2709ba2a77
http://arxiv.org/abs/2112.10789
http://arxiv.org/abs/2112.10789
Autor:
Cole Miles, Alexander Vladimirsky
In match race sailing, competitors must steer their boats upwind in the presence of unpredictably evolving weather. Combined with the tacking motion necessary to make upwind progress, this makes it natural to model their path-planning as a hybrid sto
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::940063417919ed38c68e1de2b053e307
http://arxiv.org/abs/2109.08260
http://arxiv.org/abs/2109.08260
Autor:
Cole Miles, Matthew R. Carbone, Erica J. Sturm, Deyu Lu, Andreas Weichselbaum, Kipton Barros, Robert M. Konik
We employ variational autoencoders to extract physical insight from a dataset of one-particle Anderson impurity model spectral functions. Autoencoders are trained to find a low-dimensional, latent space representation that faithfully characterizes ea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::994f8673c2d045d20e7256a3c3428b26
http://arxiv.org/abs/2107.08013
http://arxiv.org/abs/2107.08013
Autor:
Annabelle Bohrdt, Ruihan Wu, Eun-Ah Kim, Eugene Demler, Christie S. Chiu, Cole Miles, Muqing Xu, Kilian Q. Weinberger, Markus Greiner, Geoffrey Ji
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-7 (2021)
Nature Communications
Nature Communications
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. Recently, they have been successfully applied to distinguish between snapsh
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ab82296c974b3338349a42c85b7af10e
http://arxiv.org/abs/2011.03474
http://arxiv.org/abs/2011.03474