arXiv subjects

Dominik S. Kufel

Publications and source records attributed to Dominik S. Kufel.

3 recordsLinked to original sources

arXiv2026

Optimizing the dynamical preparation of quantum spin lakes on the ruby lattice

Quantum spin liquids are elusive long-range entangled states. Motivated by experiments in Rydberg quantum simulators, recent excitement has centered on the possibility of dynamically preparing a state with quantum spin liquid correlation even when the ground state phase diagram does not exhibit such a topological phase. Understanding the microscopic nature of such quantum spin "lake" states and their relationship to equilibrium spin liquid order remains an essential question. Here, we extend the use of approximately symmetric neural quantum states for real-time evolution and directly simulate the dynamical preparation in systems of up to $N=384$ atoms. We analyze a variety of spin liquid diagnostics as a function of the preparation protocol and optimize the extent of the quantum spin lake thus obtained. In the optimal case, the prepared state shows spin-liquid properties extending over half the system size, with a topological entanglement entropy plateauing close to $γ= \ln 2$. We extract two physical length scales $λ$ and $ξ$ which constrain the extent of the quantum spin lake $\ell$ from above and below.

quant-ph↗

arXiv2025

Approximately-symmetric neural networks for quantum spin liquids

We propose and analyze a family of approximately-symmetric neural networks for quantum spin liquid problems. These tailored architectures are parameter-efficient, scalable, and significantly outperform existing symmetry-unaware neural network architectures. Utilizing the mixed-field toric code and PXP Rydberg Hamiltonian models, we demonstrate that our approach is competitive with the state-of-the-art tensor network and quantum Monte Carlo methods. Moreover, at the largest system sizes (N = 480 for toric code, N=1584 for Rydberg PXP), our method allows us to explore Hamiltonians with sign problems beyond the reach of both quantum Monte Carlo and finite-size matrix-product states. The network comprises an exactly symmetric block following a non-symmetric block, which we argue learns a transformation of the ground state analogous to quasiadiabatic continuation. Our work paves the way toward investigating quantum spin liquid problems within interpretable neural network architectures.

quant-ph↗

arXiv2016

Analytical modelling of temperature effects on synapses

It was previously reported, that temperature may significantly influence neural dynamics on different levels of brain modelling. Due to this fact, while creating the model in computational neuroscience we would like to make it scalable for wide-range of various brain temperatures. However currently, because of a lack of experimental data and an absence of analytical model describing temperature influence on synapses, it is not possible to include temperature effects on multi-neuron modelling level. In this paper, we propose first step to deal with this problem: new analytical model of AMPA-type synaptic conductance, which is able to include temperature effects in low-frequency stimulations. It was constructed on basis of Markov model description of AMPA receptor kinetics and few simplifications motivated both experimentally and from Monte Carlo simulation of synaptic transmission. The model may be used for efficient and accurate implementation of temperature effects on AMPA receptor conductance in large scale neural network simulations. This in fact, opens wide-range of new possibilities for researching an influence of temperature on brain functioning.

q-bio.NC↗