collaborators

6 papers

cs.LG2026

CMT-Benchmark: A Benchmark for Condensed Matter Theory Built by Expert Researchers

Haining Pan, James V. Roggeveen, Erez Berg +16

Large language models (LLMs) have shown remarkable progress in coding and math problem-solving, but evaluation on advanced research-level problems in hard sciences remains scarce.…

cond-mat.str-el2026

Spin and Charge Conductivity in the Square Lattice Fermi-Hubbard Model

Linh Pham, Ehsan Khatami

Dynamical properties are notoriously difficult to compute in numerical treatments of the Fermi-Hubbard model, especially in two spatial dimensions. However, they are essential in p…

cond-mat.quant-gas2025

Competition of fermion pairing, magnetism, and charge order in the spin-doped attractive Hubbard gas

Thomas Hartke, Botond Oreg, Chunhan Feng +7

The tension between fermion pairing and magnetism affects numerous strongly correlated electron systems, from high-temperature cuprates to twisted bilayer graphene. Exotic forms of…

cond-mat.str-el2025

Finite-Temperature Kinetic Ferromagnetism in the Square Lattice Hubbard Model

Robin C. Newby, Ehsan Khatami

While the exact phase diagram of the Fermi-Hubbard model remains poorly understood despite decades of progress, nearly 60 years ago, Nagaoka proved that a single dopant in an other…

cond-mat.quant-gas2025

Observation of Nagaoka Polarons in a Fermi-Hubbard Quantum Simulator

Martin Lebrat, Muqing Xu, Lev Haldar Kendrick +7

Quantum interference can deeply alter the nature of many-body phases of matter. In the paradigmatic case of the Hubbard model, Nagaoka famously proved that introducing a single iti…

cond-mat.str-el2025

Autoregressive neural quantum states of Fermi Hubbard models

Eduardo Ibarra-García-Padilla, Hannah Lange, Roger G Melko +4

Neural quantum states (NQS) have emerged as a powerful ansatz for variational quantum Monte Carlo studies of strongly-correlated systems. Here, we apply recurrent neural networks (…