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pro vyhledávání: '"Miles, Cole"'
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:
http://arxiv.org/abs/2209.01265
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:
http://arxiv.org/abs/2204.07563
Autor:
Cohen-Stead, Benjamin, Bradley, Owen, Miles, Cole, Batrouni, George, Scalettar, Richard, Barros, Kipton
We introduce methodologies for highly scalable quantum Monte Carlo simulations of electron-phonon models, and report benchmark results for the Holstein model on the square lattice. The determinant quantum Monte Carlo (DQMC) method is a widely used to
Externí odkaz:
http://arxiv.org/abs/2203.01291
Autor:
Miles, Cole, Cohen-Stead, Benjamin, Bradley, Owen, Johnston, Steven, Scalettar, Richard, Barros, Kipton
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:
http://arxiv.org/abs/2201.01296
Autor:
Miles, Cole, Samajdar, Rhine, Ebadi, Sepehr, Wang, Tout T., Pichler, Hannes, Sachdev, Subir, Lukin, Mikhail D., Greiner, Markus, Weinberger, Kilian Q., Kim, Eun-Ah
Publikováno v:
Physics Review Research 5, 013026 (2023)
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:
http://arxiv.org/abs/2112.10789
Autor:
Miles, Cole, Vladimirsky, Alexander
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:
http://arxiv.org/abs/2109.08260
Autor:
Miles, Cole, Carbone, Matthew R., Sturm, Erica J., Lu, Deyu, Weichselbaum, Andreas, Barros, Kipton, Konik, Robert M.
Publikováno v:
Phys. Rev. B 104, 235111 (2021)
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:
http://arxiv.org/abs/2107.08013
Autor:
Miles, Cole, Bohrdt, Annabelle, Wu, Ruihan, Chiu, Christie, Xu, Muqing, Ji, Geoffrey, Greiner, Markus, Weinberger, Kilian Q., Demler, Eugene, Kim, Eun-Ah
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:
http://arxiv.org/abs/2011.03474
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