arXiv subjects

Eimantas Ledinauskas

Publications and source records attributed to Eimantas Ledinauskas.

7 recordsLinked to original sources

arXiv2025

Neural Importance Resampling: A Practical Sampling Strategy for Neural Quantum States

Neural quantum states (NQS) have emerged as powerful tools for simulating many-body quantum systems, but their practical use is often hindered by limitations of current sampling techniques. Markov chain Monte Carlo (MCMC) methods suffer from slow mixing and require manual tuning, while autoregressive NQS impose restrictive architectural constraints that complicate the enforcement of symmetries and the construction of determinant-based multi-state wave functions. In this work, we introduce Neural Importance Resampling (NIR), a new sampling algorithm that combines importance resampling with a separately trained autoregressive proposal network. This approach enables efficient and unbiased sampling without constraining the NQS architecture. We demonstrate that NIR supports stable and scalable training, including for multi-state NQS, and mitigates issues faced by MCMC and autoregressive approaches. Numerical experiments on the 2D transverse-field Ising and $J_1$-$J_2$ Heisenberg models show that NIR outperforms MCMC in challenging regimes and yields results competitive with state of the art methods. Our results establish NIR as a robust alternative for sampling in variational NQS algorithms.

quant-ph↗

arXiv2024

Universal Performance Gap of Neural Quantum States Applied to the Hofstadter-Bose-Hubbard Model

Neural Quantum States (NQS) have demonstrated significant potential in approximating ground states of many-body quantum systems, though their performance can be inconsistent across different models. This study investigates the performance of NQS in approximating the ground state of the Hofstadter-Bose-Hubbard (HBH) model, an interacting boson system on a two-dimensional square lattice with a perpendicular magnetic field. Our results indicate that increasing magnetic flux leads to a substantial increase in energy error, up to three orders of magnitude. Importantly, this decline in NQS performance is consistent across different optimization methods, neural network architectures, and physical model parameters, suggesting a significant challenge intrinsic to the model. Despite investigating potential causes such as wave function phase structure, quantum entanglement, fractional quantum Hall effect, and the variational loss landscape, the precise reasons for this degradation remain elusive. The HBH model thus proves to be an effective testing ground for exploring the capabilities and limitations of NQS. Our study highlights the need for advanced theoretical frameworks to better understand the expressive power of NQS which would allow a systematic development of methods that could potentially overcome these challenges.

quant-ph↗

arXiv2023

Scalable Imaginary Time Evolution with Neural Network Quantum States

The representation of a quantum wave function as a neural network quantum state (NQS) provides a powerful variational ansatz for finding the ground states of many-body quantum systems. Nevertheless, due to the complex variational landscape, traditional methods often employ the computation of quantum geometric tensor, consequently complicating optimization techniques. Contributing to efforts aiming to formulate alternative methods, we introduce an approach that bypasses the computation of the metric tensor and instead relies exclusively on first-order gradient descent with Euclidean metric. This allows for the application of larger neural networks and the use of more standard optimization methods from other machine learning domains. Our approach leverages the principle of imaginary time evolution by constructing a target wave function derived from the Schrödinger equation, and then training the neural network to approximate this target. We make this method adaptive and stable by determining the optimal time step and keeping the target fixed until the energy of the NQS decreases. We demonstrate the benefits of our scheme via numerical experiments with 2D J1-J2 Heisenberg model, which showcase enhanced stability and energy accuracy in comparison to direct energy loss minimization. Importantly, our approach displays competitiveness with the well-established density matrix renormalization group method and NQS optimization with stochastic reconfiguration.

quant-ph↗

arXiv2022

Automatic travel pattern extraction from visa page stamps using CNN models

Manual travel pattern inference from visa page stamps is a time consuming activity and constitutes an important bottleneck in the efficiency of traveler inspection at border crossings. Despite efforts to digitize and record the border crossing information into databases, travel pattern inference from stamps will remain a problem until every country in the world is incorporated into such a unified system. This could take decades. We propose an automated document analysis system that processes scanned visa pages and automatically extracts the travel pattern from detected stamps. The system processes the page via the following pipeline: stamp detection in the visa page; general stamp country and entry/exit recognition; Schengen area stamp country and entry/exit recognition; Schengen area stamp date extraction. For each stage of the proposed pipeline we construct neural network models and train then on a mixture of real and synthetic data. We integrated Schengen area stamp detection and date, country, entry/exit recognition models together with a graphical user interface into a prototype of an automatic travel pattern extraction tool. We find that by combining simple neural network models into our proposed pipeline a useful tool can be created which can speed up the travel pattern extraction significantly.

cs.CV↗

arXiv2020

Training Deep Spiking Neural Networks

Computation using brain-inspired spiking neural networks (SNNs) with neuromorphic hardware may offer orders of magnitude higher energy efficiency compared to the current analog neural networks (ANNs). Unfortunately, training SNNs with the same number of layers as state of the art ANNs remains a challenge. To our knowledge the only method which is successful in this regard is supervised training of ANN and then converting it to SNN. In this work we directly train deep SNNs using backpropagation with surrogate gradient and find that due to implicitly recurrent nature of feed forward SNN's the exploding or vanishing gradient problem severely hinders their training. We show that this problem can be solved by tuning the surrogate gradient function. We also propose using batch normalization from ANN literature on input currents of SNN neurons. Using these improvements we show that is is possible to train SNN with ResNet50 architecture on CIFAR100 and Imagenette object recognition datasets. The trained SNN falls behind in accuracy compared to analogous ANN but requires several orders of magnitude less inference time steps (as low as 10) to reach good accuracy compared to SNNs obtained by conversion from ANN which require on the order of 1000 time steps.

cs.NE↗

arXiv2020

Evolution of dwarf galaxy observable parameters

We present a semi-analytic model of isolated dwarf galaxy evolution and use it to study the build-up of observed correlations between dwarf galaxy properties. We analyse the evolution using models with averaged and individual halo mass assembly histories in order to determine the importance of stochasticity on the present-day properties of dwarf galaxies. The model has a few free parameters, but when these are calibrated using the halo mass - stellar mass and stellar mass-metallicity relations, the results agree with other observed dwarf galaxy properties remarkably well. Redshift evolution shows that even isolated galaxies change significantly over the Hubble time and that 'fossil dwarf galaxies' with properties equivalent to those of high-redshift analogues should be extremely rare, or non-existent, in the Local Universe. A break in most galaxy property correlations develops over time, at a stellar mass $M_* \simeq 10^7 M_\odot$. It is caused predominantly by the ionizing background radiation and can therefore in principle be used to constrain the properties of reionization.

astro-ph.GA↗

arXiv2018

Reignited star formation in dwarf galaxies quenched during reionization

Irregular dwarf galaxies of the Local Group have very varied properties and star formation histories. Some of them formed the majority of their stars very late compared to the others. Extreme examples are Leo A and Aquarius which reached the peak of star formation at $z<1$ ( > 6 Gyr after BB). This fact seemingly challenges the LCDM cosmology because the dark matter halos of these galaxies on average should assemble the majority of their masses before z~2 (<3 Gyr after BB). In this work we investigate whether the delayed star formation histories of some irregular dwarf galaxies could be explained purely by the stochasticity of their mass assembly histories coupled with the effect of cosmic reionization. We develop a semi-analytic model to follow the accretion of baryonic matter, star formation and stellar feedback in dark matter halos with present day virial masses 10^9 M_Sun < M < 10^11 M_Sun and with different stochastic growth histories obtained using the PINOCCHIO code based on Lagrangian perturbation theory. We obtain the distributions of observable parameters and the evolution histories for these galaxies. Accretion of baryonic matter is strongly suppressed after the epoch of reionization in some models but they continue to accrete dark matter and eventually reach enough mass for accretion of baryonic matter to begin again. These "reborn" model galaxies show very similar delayed star formation histories to those of Leo A and Aquarius. We find that the stochasticity caused by mass assembly histories is enhanced in systems with virial masses ~10^10 M_Sun because of their sensitivity to the photoionizing intergalactic radiation field after the epoch of reionization. This results in qualitatively different star formation histories in late- and early-forming galaxies and it might explain the peculiar star formation histories of irregular dwarf galaxies such as Leo A and Aquarius.

astro-ph.GA↗