arXiv subjects

Jarosław Pawłowski

Publications and source records attributed to Jarosław Pawłowski.

At least 19 recordsLinked to original sources

arXiv2026

Geometry-Induced Skin Effect in Electron Hydrodynamics

In ultra-clean 2d materials electron viscosity is as important as Ohmic dissipation and electron transport exhibits hydrodynamic features. Using a simple framework of Brinkman equations we find that hydrodynamic electron flows give rise to a geometric skin effect: sharp obstacles locally enhance the current suppressing it far from the edges where the flow is unobstructed. This effect arises within hydrodynamic transport with finite momentum relaxation and does not rely on ballistic dynamics. Our results provide a natural hydrodynamic interpretation of edge-enhanced and double-bump current profiles observed in constricted geometries. By comparing with recent scanning NV magnetometry experiments on gated graphene, we demonstrate that such flow patterns are consistent with viscous hydrodynamics shaped by geometry, clarifying the role of geometric effects in the interpretation of electronic flow experiments.

cond-mat.mes-hall↗

arXiv2026

Learning quantum tomography from incomplete measurements

We revisit quantum tomography in an informationally incomplete scenario and propose improved state reconstruction methods using deep neural networks. In the first approach, the trained network predicts an optimal linear or quadratic reconstructor with coefficients depending only on the collection of (already taken) measurement operators. This effectively refines the undercomplete tomographic reconstructor based on pseudoinverse operation. The second, based on an LSTM recurrent network performs state reconstruction sequentially. It can also optimize the measurement sequence, which suggests a no-free-lunch theorem for tomography: by narrowing the state space, we gain the possibility of more efficient tomography by learning the optimal sequence of measurements. Numerical experiments for a 2-qubit system show that both methods outperform standard maximum likelihood estimation and also scale to larger 3- and 4-qubit systems. Our results demonstrate that neural networks can effectively learn the underlying geometry of multi-qubit states and use it for their reconstruction.

quant-ph↗

arXiv2026

Learning Hamiltonians for solid-state quantum simulators

We introduce a generalizable framework for identifying effective Hamiltonians directly from experimental data in solid-state quantum systems. Our unsupervised autoencoder-based approach incorporates the governing physics (here, the S-matrix formalism) directly into the decoder, enabling physically meaningful Hamiltonian inference without labeled Hamiltonian parameters. Through numerical experiments on a triple quantum dot chain, we demonstrate automated characterization of programmable solid-state simulators from transport measurements. The model accurately infers Hamiltonian parameters and generalizes beyond the training domain, while exhibiting finite capacity as the parameter space becomes increasingly broad or diverse. We further demonstrate robustness to noisy measurements and show that the model can be successfully trained on transport data generated outside the Hamiltonian family assumed by the physics decoder, while still identifying meaningful effective parameters.

cond-mat.mes-hall↗

arXiv2026

Information in Many-body Eigenstates: A Question of Learnability

To what extent do individual eigenstates encode information about their parent Hamiltonian, and how does this encoding vary across the spectrum? We introduce \emph{learnability} as a new framework to quantify this information, measured by the precision with which a machine learning model can reconstruct a Hamiltonian from a limited set of eigenstates. For many-body quantum systems, there is a contrast between the eigenstates near the spectral edges (low-entanglement, highly-structured states) and those far from the spectral edges (high-entanglement, near-random states). Using an encoder-decoder neural network for a non-integrable spin chain, we show that this dichotomy results in a stark difference in learnability: spectral-edge eigenstates allow for higher-accuracy Hamiltonian reconstruction using significantly fewer eigenstates, compared to mid-spectrum eigenstates. Our results provide a new lens through which to view the spectral structure of many-body systems.

quant-ph↗

arXiv2026

AI-enhanced tuning of quantum dot Hamiltonians toward Majorana modes

We propose a neural network-based model capable of learning the broad landscape of working regimes in quantum dot simulators, and using this knowledge to autotune these devices - based on transport measurements - toward obtaining Majorana modes in the structure. The model is trained in an unsupervised manner on synthetic data in the form of conductance maps, using a physics-informed loss that incorporates key properties of Majorana zero modes. We show that, with appropriate training, a deep vision-transformer network can efficiently memorize relation between Hamiltonian parameters and structures on conductance maps and use it to propose parameters update for a quantum dot chain that drive the system toward topological phase. Starting from a broad range of initial detunings in parameter space, a single update step is sufficient to generate nontrivial zero modes. Moreover, by enabling an iterative tuning procedure - where the system acquires updated conductance maps at each step - we demonstrate that the method can address a much larger region of the parameter space.

cond-mat.mes-hall↗

arXiv2025

Lift force in chiral, compressible granular matter

Micropolar fluid theory, an extension of classical Newtonian fluid dynamics, incorporates angular velocities and rotational inertias and has long been a foundational framework for describing granular flows. We propose a macroscopic model of granular matter based on micropolar fluid dynamics, which incorporates internal rotations, couple stresses, and broken parity through odd viscosity. Our framework extends traditional micropolar theory to describe chiral granular flows driven far from equilibrium, where energy is continuously injected and dissipated. In particular, we focus on steady states and explicitly neglect energy conservation, reflecting the dissipative nature of granular systems maintained in non-equilibrium by external forcing. Within this setup, we study the lift force experienced by a test bead embedded in a compressible, parity-breaking granular flow. We analyze how odd viscosity and microrotation modify the transverse forces, using both analytical results in the linearized Stokes regime and nonlinear finite element simulations. Our results demonstrate that micropolar fluids provide a physically consistent and symmetry-informed continuum description of chiral granular matter, capable of capturing lift forces that emerge uniquely from odd transport effects.

cond-mat.soft↗

arXiv2025

Learning entanglement from tomography data: contradictory measurement importance for neural networks and random forests

We study the effectiveness of two distinct machine learning techniques, neural networks and random forests, in the quantification of entanglement from two-qubit tomography data. Although we predictably find that neural networks yield better accuracy, we also find that the way that the two methods reach their prediction is starkly different. This is seen by the measurements which arthe most important for the classification. Neural networks follow the intuitive prediction that measurements containing information about non-local coherences are most important for entanglement, but random forests signify the dominance of information contained in occupation measurements. This is because occupation measurements are necessary for the extraction of data about all other density matrix elements from the remaining measurements. The same discrepancy does not occur when the models are used to learn entanglement directly from the elements of the density matrix, so it is the result of the scattering of information and interdependence of measurement data. As a result, the models behave differently when noise is introduced to various measurements, which can be harnessed to obtain more reliable information about entanglement from noisy tomography data.

quant-ph↗

arXiv2024

Electrically tunable MoSe$_2$/WSe$_2$ heterostructure-based quantum dot

We describe here a theory of a quantum dot in an electrically tunable MoSe$_2$/WSe$_2$ heterostructure. Van der Waals heterostructures allow for tuning their electronic properties beyond their monolayer counterparts. We start by determining their electronic structure using density functional theory. We obtain the type-II band alignment and close in energy conduction band minima (valleys) at the $K$ and $Q$ points in the Brillouin zone. The valence band maxima, also energetically close, are located at the $K$ and $Γ$ points. By analyzing the Kohn-Sham wavefunctions, we describe the layer, spin, and orbital contributions. Next, we construct an \textit{ab initio}-based tight-binding model, which helps us to better understand the complexity of the interlayer interactions. We determine the effect of a vertical electric field, showing that vertical gating enables control of valleys extrema and their occupancy. Finally, we employ the tight-binding model to investigate laterally gated quantum dots and analyze the influence of a perpendicular electric field on their energy spectrum. Our results demonstrate that tuning the electric field enables control over the valley character of the quantum dot states, selectively localizing them in either the $K$ or $Q$ valleys, as evidenced by their characteristic degeneracies and wavefunctions.

cond-mat.mes-hall↗

arXiv2024

Valley-spin polarization at zero magnetic field induced by strong hole-hole interactions in monolayer WSe$_2$

Monolayer transition metal dichalcogenides have emerged as prominent candidates to explore the complex interplay between the spin and the valleys degrees of freedom. The strong spin-orbit interaction and broken inversion symmetry within these materials lead to the spin-valley locking effect, in which carriers occupying the K and K' valleys of the reciprocal space must have opposite spin depending on which valley they reside. This effect is particularly strong for holes due to a larger spin-orbit gap in the valence band. By reducing the dimensionality of a monolayer of tungsten diselenide to 1D via electrostatic confinement, we demonstrate that spin-valley locking in combination with strong hole-hole interactions lead to a ferromagnetic state in which hole transport through the 1D system is spin-valley polarized, even without an applied magnetic field, and that the persistence of this spin-valley polarized configuration can be tuned by a global back-gate. This observation opens the possibility of implementing a robust and stable valley polarized system, essential for valleytronic applications.

cond-mat.mes-hall↗

arXiv2024

Interacting holes in a gated WSe$_2$ quantum channel: valley correlations and zigzag Wigner crystal

We present a theory of interacting valence holes in a gate-defined one-dimensional quantum channel in a single layer of a transition metal dichalcogenide material WSe$_2$. Based on a microscopic atomistic tight-binding model and Hartree-Fock and exact configuration-interaction tools we demonstrate the possibility of symmetry-broken valley polarized states for strongly interacting holes. The interplay between interactions, perpendicular magnetic field, and the lateral confinement asymmetry together with the strong Rashba spin-orbit coupling present in WSe$_2$ material is analyzed, and its impact on valley polarization is discussed. For weaker interactions, an investigation of the pair correlation function reveals a valley-antiferromagnetic phase. For low hole densities, a formation of a zigzag Wigner crystal phase is predicted. The impact of various hole liquid phases on transport in a high mobility quasi-one dimensional channel is discussed.

cond-mat.mes-hall↗

arXiv2024

Electrical manipulation of valley-qubit and valley geometric phase in lateral monolayer heterostructures

We explore a solid state qubit defined on valley isospin of an electron confined in a gate-defined quantum dot created in an area of monolayer MoS$_2$/WS$_2$ lateral junction, where a steep dipolar potential emerges. We show that the junction oriented along an armchair direction can induce intervalley transitions of the electron confined in the neighboring quantum dot when the (gate-controllable) overlapping with the junction is significant and pumping frequency tuned. The pumping scheme that induces transitions is all-electrical: obtained by applying oscillating voltages to control gates and thus enables for scalable qubit architectures. We also report another possibility of valley-qubit manipulation by accumulating non-Abelian valley Berry phase. To model nanodevice we solve the time-dependent Schrödinger-Poisson equations in a tight-binding approach and obtain exact time-evolution of the valley-qubit system.

cond-mat.mes-hall↗

arXiv2024

Identification of quantum entanglement with Siamese convolutional neural networks and semi-supervised learning

Quantum entanglement is a fundamental property commonly used in various quantum information protocols and algorithms. Nonetheless, the problem of identifying entanglement has still not reached a general solution for systems larger than $2\times3$. In this study, we use deep convolutional NNs, a type of supervised machine learning, to identify quantum entanglement for any bipartition in a 3-qubit system. We demonstrate that training the model on synthetically generated datasets of random density matrices excluding challenging positive-under-partial-transposition entangled states (PPTES), which cannot be identified (and correctly labeled) in general, leads to good model accuracy even for PPTES states, that were outside the training data. Our aim is to enhance the model's generalization on PPTES. By applying entanglement-preserving symmetry operations through a triple Siamese network trained in a semi-supervised manner, we improve the model's accuracy and ability to recognize PPTES. Moreover, by constructing an ensemble of Siamese models, even better generalization is observed, in analogy with the idea of finding separate types of entanglement witnesses for different classes of states.

quant-ph↗

arXiv2024

Data-driven criteria for quantum correlations

We build a machine learning model to detect correlations in a three-qubit system using a neural network trained in an unsupervised manner on randomly generated states. The network is forced to recognize separable states, and correlated states are detected as anomalies. Quite surprisingly, we find that the proposed detector performs much better at distinguishing a weaker form of quantum correlations, namely, the quantum discord, than entanglement. In fact, it has a tendency to grossly overestimate the set of entangled states even at the optimal threshold for entanglement detection, while it underestimates the set of discordant states to a much lesser extent. In order to illustrate the nature of states classified as quantum-correlated, we construct a diagram containing various types of states -- entangled, as well as separable, both discordant and non-discordant. We find that the near-zero value of the recognition loss reproduces the shape of the non-discordant separable states with high accuracy, especially considering the non-trivial shape of this set on the diagram. The network architecture is designed carefully: it preserves separability, and its output is equivariant with respect to qubit permutations. We show that the choice of architecture is important to get the highest detection accuracy, much better than for a baseline model that just utilizes a partial trace operation.

quant-ph↗

arXiv2024

DMRG Analysis of Magnetic Order in the Zigzag Edges of Hexagonal CrN Nanoribbons

We investigate the finite temperature magnetic order at the edges of hexagonal CrN nanoribbons by using the density-functional theory combined with the density-matrix renormalization group method. Moreover, the spin-dependent transport in nanoribbons is calculated within the semi-classical Boltzmann transport theory. We find out that the zigzag edges have lower energy with respect to armchair edges. The zigzag edge of CrN nanoribbon shows half metallic electronic character which is the same as for the 2D monolayer. The localized electronic states on the zigzag edges reduce the electronic band gap energy for spin down electrons. The ab-initio electronic results are mapped into an effective 1D Heisenberg spin model up to the next nearest neighbor exchange interaction term. For zigzag ribbons, the nearest neighbor and next nearest neighbor magnetic exchange are around $10$ to $12$, and $-2$ to $0$~meV/Cr atom, respectively. The finite spin correlation length in 1D nanoribbons drops sharply to zero with temperature. The absence of long range spin correlations at the edges is a practical drawback for future room temperature 2D spintronic devices. The maximally localized Wannier functions are used for band interpolation and spin-dependent transport calculations by using the semi-classical Boltzmann equation. We show that zigzag edges of CrN are perfect spin filter under both electron and hole doping.

cond-mat.mes-hall↗

arXiv2024

Machine-learned models for magnetic materials

We present a general framework for modeling power magnetic materials characteristics using deep neural networks. Magnetic materials represented by multidimensional characteristics (that mimic measurements) are used to train the neural autoencoder model in an unsupervised manner. The encoder is trying to predict the material parameters of a theoretical model, which is then used in a decoder part. The decoder, using the predicted parameters, reconstructs the input characteristics. The neural model is trained to capture a synthetically generated set of characteristics that can cover a broad range of material behaviors, leading to a model that can generalize on the underlying physics rather than just optimize the model parameters for a single measurement. After setting up the model, we prove its usefulness in the complex problem of modeling magnetic materials in the frequency and current (out-of-linear range) domains simultaneously, for which we use measured characteristics obtained for frequency up to $10$ MHz and H-field up to saturation.

cond-mat.mtrl-sci↗

arXiv2024

Single Electron Quantum Dot in Two-Dimensional Transition Metal Dichalcogenides

Spin-valley properties in two-dimensional (2D) semiconducting transition metal dichalcogenides (TMDC) has attracted significant interest due to the possible applications in quantum computing. Spin-valley properties can be exploited in TMDC quantum dot (QD) with well-resolved energy levels. This requires smaller QDs, especially in material systems with heavy carrier effective mass e.g. TMDCs and silicon. Device architectures employed for TMDC QDs so far have difficulty achieving smaller QDs. Therefore, an alternative approach in the device architecture is needed. Here, we propose a multilayer device architecture to achieve a gate-defined QD in TMDC with a relatively large energy splitting on the QD. We provide a range of device dimensions and dielectric thicknesses and its correlation with the QD energy splitting. The device architecture is modeled realistically. Moreover, we show that all the device parameters used in modeling are experimentally achievable. These studies lay the foundation for future work toward spin-valley qubits in TMDCs. The successful implementation of these device architectures will drive the technological development of 2D materials-based quantum technologies.

cond-mat.mes-hall↗

arXiv2022

Generation of microbial colonies dataset with deep learning style transfer

We introduce an effective strategy to generate an annotated synthetic dataset of microbiological images of Petri dishes that can be used to train deep learning models in a fully supervised fashion. The developed generator employs traditional computer vision algorithms together with a neural style transfer method for data augmentation. We show that the method is able to synthesize a dataset of realistic looking images that can be used to train a neural network model capable of localising, segmenting, and classifying five different microbial species. Our method requires significantly fewer resources to obtain a useful dataset than collecting and labeling a whole large set of real images with annotations. We show that starting with only 100 real images, we can generate data to train a detector that achieves comparable results (detection mAP = 0.416, and counting MAE = 4.49) to the same detector but trained on a real, several dozen times bigger dataset (mAP = 0.520, MAE = 4.31), containing over 7k images. We prove the usefulness of the method in microbe detection and segmentation, but we expect that it is general and flexible and can also be applicable in other domains of science and industry to detect various objects.

cs.CV↗

arXiv2021

Spin-valley qubits in gated quantum dots in a single layer of transition metal dichalcogenides

We develop a microscopic and atomistic theory of electron spin-based qubits in gated quantum dots in a single layer of transition metal dichalcogenides. The qubits are identified with two degenerate locked spin and valley states in a gated quantum dot. The two-qubit states are accurately described using a multi-million atom tight-binding model solved in wavevector space. The spin-valley locking and strong spin-orbit coupling result in two degenerate states, one of the qubit states being spin-down located at the $+K$ valley of the Brillouin zone, and the other state located at the $-K$ valley with spin up. We describe the qubit operations necessary to rotate the spin-valley qubit as a combination of the applied vertical electric field, enabling spin-orbit coupling in a single valley, with a lateral strongly localized valley-mixing gate.

cond-mat.mes-hall↗