arXiv subjects

Erik Fitzner

Publications and source records attributed to Erik Fitzner.

3 recordsLinked to original sources

arXiv2026

Finite-temperature spin diffusion in the two-dimensional XY model

We present a combined theory-experiment study to quantify spin diffusion in the square lattice quantum spin-1/2 XY model at finite temperature. On the theory side, we leverage a recently developed dynamical high-temperature expansion method to faithfully capture the long spatiotemporal scales of the hydrodynamic regime. Experimental results are obtained from an optical lattice hard-core boson quantum simulator. The excellent agreement of spin diffusion constants marks a breakthrough in spin-transport beyond one dimension and for the quantitative validation of state-of-the-art quantum simulation platforms. We also provide theory predictions for future experiments on dynamic spin conductivity or anisotropy-induced integrability breaking.

cond-mat.quant-gas↗

arXiv2026

Thermometry for a Kagome Lattice Dipolar Rydberg Simulator

We propose an accurate thermometry approach for Rydberg atom tweezer arrays combining data from correlation and local susceptibility measurements with a theoretical high-temperature expansion method for dynamic spin correlations. We apply our approach to a recent quantum simulation experiment [Bornet et al., arXiv 2602.14323] realizing an anti-ferromagnetic dipolar spin-1/2 XY model on the Kagome lattice. We obtain T=0.55J and S/N=0.67 ln2 for temperature and entropy respectively, showing that further experimental efforts are required to reach the putative quantum spin liquid regime.

cond-mat.quant-gas↗

arXiv2026

Detecting nonequilibrium phase transitions via continuous monitoring of space-time trajectories and autoencoder-based clustering

The characterization of collective behavior and nonequilibrium phase transitions in quantum systems is typically rooted in the analysis of suitable system observables, so-called order parameters. These observables might not be known a priori, but they may in principle be identified through analyzing the quantum state of the system. Experimentally, this can be particularly demanding as estimating quantum states and expectation values of quantum observables requires a large number of projective measurements. However, open quantum systems can be probed in situ by monitoring their output, e.g. via heterodyne-detection or photon-counting experiments, which provide space-time resolved information about their dynamics. Building on this, we present a machine-learning approach to detect nonequilibrium phase transitions from the measurement time-records of continuously-monitored quantum systems. We benchmark our method using the quantum contact process, a model featuring an absorbing-state phase transition, which constitutes a particularly challenging test case for the quantum simulation of nonequilibrium processes.

quant-ph↗