56 citations · 237 across the 8 of their papers we have counts for
9 papers
Sunny.jl: A Julia Package for Spin Dynamics
David Dahlbom, Hao Zhang, Cole Miles +16
Sunny is a Julia package designed to serve the needs of the quantum magnetism community. It supports the specification of a very broad class of spin models and a diverse suite of n…
Langevin dynamics of generalized spins as SU() coherent states
David Dahlbom, Cole Miles, Hao Zhang +2
Classical models of spin systems traditionally retain only the dipole moments, but a quantum spin state will frequently have additional structure. Spins of magnitude have $N=2S…
Geometric integration of classical spin dynamics via a mean-field Schrödinger equation
David Dahlbom, Hao Zhang, Cole Miles +3
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 th…
Fast and scalable quantum Monte Carlo simulations of electron-phonon models
Benjamin Cohen-Stead, Owen Bradley, Cole Miles +3
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.…
Dynamical tuning of the chemical potential to achieve a target particle number in grand canonical Monte Carlo simulations
Cole Miles, Benjamin Cohen-Stead, Owen Bradley +3
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 chemi…
Machine learning discovery of new phases in programmable quantum simulator snapshots
Cole Miles, Rhine Samajdar, Sepehr Ebadi +7
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 poss…
Stochastic Optimal Control of a Sailboat
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 progre…
Machine learning of Kondo physics using variational autoencoders and symbolic regression
Cole Miles, Matthew R. Carbone, Erica J. Sturm +4
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-dim…
Correlator Convolutional Neural Networks: An Interpretable Architecture for Image-like Quantum Matter Data
Cole Miles, Annabelle Bohrdt, Ruihan Wu +7
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. Recen…