105 citations · 142 across the 8 of their papers we have counts for
9 papers
The Blume-Capel Model as a Field Theory: A Monte Carlo and Flow Model Study
Ejaaz Merali, Ryan Larson, Ehsan Khatami +1
We report Monte Carlo simulations of a continuous spin variant of the Blume-Capel model. We compute the phase diagrams in the crystal-field-temperature plane, as a function of the…
Impact of strain and dark states on spectroscopic measurements of silicon-vacancy centers in diamond
Tommy Chin, Kesav V. Narayan, Imran Bashir +4
Negatively charged silicon-vacancy (SiV) centers in diamond offer an attractive platform for the development of many forms of quantum technology. However, questions remain in c…
Parallel Scan Recurrent Neural Quantum States for Scalable Variational Monte Carlo
Ejaaz Merali, Mohamed Hibat-Allah, Mohammad Kohandel +2
Neural-network quantum states have emerged as a powerful variational framework for quantum many-body systems, with recent progress often driven by massively parallel architectures…
Graduate Training in Quantum Information Science and Engineering: Lessons, Challenges, and a Roadmap from the NSF Research Traineeship Programs
Yohannes Abate, Victor Acosta, Alessandro Alabastri +31
Since 2019, eighteen NSF Research Traineeship (NRT) awards in quantum information science and engineering (QISE) and adjacent fields have been funded, constituting the largest NSF-…
Finite-Temperature Ferromagnetic Correlations of the Kagome Lattice Hubbard Model
Francisco Correia, Kyle Corbett, Ehsan Khatami
The Kagome lattice Fermi-Hubbard model is one of the most physically rich, and at the same time most challenging, models to study in strongly-correlated physics. Among its special…
Charge Order in the half-filled bond-Holstein Model
Charles Jordan, George Issa, Ehsan Khatami +3
We use determinant quantum Monte Carlo to study the half-filled `bond-Holstein' model on a square lattice. We find that the model exhibits a charge-density-wave (CDW) phase transit…
Learning by Confusion: The Phase Diagram of the Holstein Model
George Issa, Owen Bradley, Ehsan Khatami +1
We employ the "learning by confusion" technique, an unsupervised machine learning approach for detecting phase transitions, to analyze quantum Monte Carlo simulations of the two-di…
A perspective on machine learning and data science for strongly correlated electron problems
S. Johnston, E. Khatami, R. T. Scalettar
Numerical approaches to the correlated electron problem have achieved considerable success, yet are still constrained by several bottlenecks, including high order polynomial or exp…
Geometry Dependence of the Sign Problem
V. I. Iglovikov, E. Khatami, R. T. Scalettar
The sign problem is the fundamental limitation to quantum Monte Carlo simulations of the statistical mechanics of interacting fermions. Determinant quantum Monte Carlo (DQMC) is on…