arXiv subjects

Tomasz Szołdra

Publications and source records attributed to Tomasz Szołdra.

9 recordsLinked to original sources

arXiv2026

Belief Propagation-based Disentanglers for Tensor Network State Preparation

We develop a quantum circuit synthesis method for preparing a class of tensor network states. The scheme applies to states tractable with belief propagation (BP), a tensor network gauging scheme which recently allowed for classical simulations at large scales. The problem is reduced to independent, strictly local, classical variational optimizations: each nearest-neighbor two-qubit "disentangler" gate minimizes the entropy defined on an edge. Disentanglers drive the state to a product state and their Hermitian conjugate prepares the target. Each disentangling layer has depth at most $z+1$ (with $z$ the maximal number of nearest neighbors per site), the optimization has no barren plateaus, and the bond dimension stays bounded. As a demonstration, with only $3$-$5$ disentangling layers we prepare a $102$-qubit tree tensor network encoding a $17$-dimensional normal distribution and the transverse-field Ising model ground states on a $64$- to $127$-qubit heavy-hex lattice with fidelities of order $0.9-0.999$. The method opens new possibilities for quantum applications by transferring classical tensor network states onto hardware.

quant-ph↗

arXiv2026

Recurrence analysis of quantum many-body dynamics

Observables of out-of-equilibrium quantum many-body systems display complex temporal behavior that encodes the underlying physical mechanisms but typically resists straightforward interpretations. We introduce recurrence analysis - a nonlinear time-series analysis framework long established for classical dynamical systems - to investigate correlated quantum many-body dynamics. Recurrence plots provide a qualitative fingerprint of simulated or experimental data, while recurrence quantification analysis extracts corresponding numerical descriptors. Applying this framework to quenches from the paramagnetic ground state in the one-dimensional transverse-field Ising model, we observe a clear progression in the recurrence plots of two-site correlations: nearly periodic patterns in the deeply ferromagnetic phase give way to multiscale temporal structures at criticality. Recurrence quantifiers further recover the critical field strength without prior knowledge of the model, establishing recurrence analysis as a versatile tool for characterizing quantum many-body dynamics, including unsupervised detection of quantum phase transitions.

quant-ph↗

arXiv2026

Scalable Preparation of Matrix Product States with Sequential and Brick Wall Quantum Circuits

Preparing arbitrary quantum states requires exponential resources. Matrix Product States (MPS) admit more efficient constructions, particularly when accuracy is traded for circuit complexity. Existing approaches to MPS preparation mostly rely on heuristic circuits that are deterministic but quickly saturate in accuracy, or on variational optimization methods that reach high fidelities but scale poorly. This work introduces an end-to-end MPS preparation framework that combines the strengths of both strategies within a single pipeline. Heuristic staircase-like and brick wall disentangler circuits provide warm-start initializations for variational optimization, enabling high-fidelity state preparation for large systems. Target MPSs are either specified as physical quantum states or constructed from classical datasets via amplitude encoding, using step-by-step singular value decompositions or tensor cross interpolation. The framework incorporates entanglement-based qubit reordering, reformulated as a quadratic assignment problem, and low-level optimizations that reduce depths by up to 50% and CNOT counts by 33%. We evaluate the full pipeline on datasets of varying complexity across systems of 19-50 qubits and identify trade-offs between fidelity, gate count, and circuit depth. Optimized brick wall circuits typically achieve the lowest depths, while the optimized staircase-like circuits minimize gate counts. Overall, our results provide principled and scalable protocols for preparing MPSs as quantum circuits, supporting utility-scale applications on near-term quantum devices.

quant-ph↗

arXiv2024

Catching thermal avalanches in the disordered XXZ model

We study the XXZ model with a random magnetic field in contact with a weakly disordered spin chain, acting as a finite thermal bath. We revise Fermi's golden rule description of the interaction between the thermal bath and the XXZ spin chain, contrasting it with a nonperturbative quantum avalanche scenario for the thermalization of the system. We employ two-point correlation functions to define the extent $ξ_d$ of the thermalized region next to the bath. Unbounded growth of $ξ_d$ proportional to the logarithm of time or faster is a signature of an avalanche. Such behavior signifies the thermalization of the system, as we confirm numerically for a generic initial state in the ergodic and critical regimes of the XXZ spin chain. In the many-body localized regime, a clear termination of avalanches is observed for specifically prepared initial states and, surprisingly, is not visible for generic initial product states. Additionally, we extract the localization length of the local integrals of motion and show that a bath made out of a weakly disordered XXZ chain has a similar effect on the system as a bath modeled by a Hamiltonian from a Gaussian orthogonal ensemble of random matrices. We also comment on the result of the earlier study (Phys. Rev. B 108, L020201 (2023)), arguing that the observed thermalization is due to external driving of the system and does not occur in the autonomous model. Our work reveals experimentally accessible signatures of quantum avalanches and identifies conditions under which termination of the avalanches may be observed.

cond-mat.dis-nn↗

arXiv2023

Fidelity and Overlap of Neural Quantum States: Error Bounds on the Monte Carlo Estimator

Overlap between two neural quantum states can be computed through Monte Carlo sampling by evaluating the unnormalized probability amplitudes on a subset of basis configurations. Due to the presence of probability amplitude ratios in the estimator, which are possibly unbounded, convergence of this quantity is not immediately obvious. Our work provides a derivation of analytical error bounds on the overlap in the Monte Carlo calculations as a function of their fidelity and the number of samples. Special case of normalized autoregressive neural quantum states is analyzed separately.

quant-ph↗

arXiv2023

Femtosecond pulse parameter estimation from photoelectron momenta using machine learning

Deep learning models have provided huge interpretation power for image-like data. Specifically, convolutional neural networks (CNNs) have demonstrated incredible acuity for tasks such as feature extraction or parameter estimation. Here we test CNNs on strong-field ionization photoelectron spectra, training on theoretical data sets to `invert' experimental data. Pulse characterization is used as a `testing ground', specifically we retrieve the laser intensity, where `traditional' measurements typically lead to 20% uncertainty. We report on crucial data augmentation techniques required to successfully train on theoretical data and return consistent results from experiments, including accounting for detector saturation. The same procedure can be repeated to apply CNNs in a range of scenarios for strong-field ionization. Using a predictive uncertainty estimation, reliable laser intensity uncertainties of a few percent can be extracted, which are consistently lower than those given by traditional techniques. Using interpretability methods can reveal parts of the distribution that are most sensitive to laser intensity, which can be directly associated with holographic interferences. The CNNs employed provide an accurate and convenient ways to extract parameters, and represent a novel interpretational tool for strong-field ionization spectra.

physics.atom-ph↗

arXiv2023

Tracking locality in time evolution of disordered systems

Using local density correlation functions for a one-dimensional spin system, we introduce a correlation function difference (CFD) which compares correlations on a given site between a full system of size $L$ and its restriction to $\ell<L$ sites. We show that CFD provides useful information on transfer of information in quantum many-body systems by considering the examples of ergodic, Anderson, and many-body localized regimes in disordered XXZ spin chain. In the ergodic phase, we find that the propagation of CFD is asymptotically faster than the spin transport but slower than the ballistic propagation implied by the Lieb-Robinson bound. In contrast, in the localized cases, we unravel an exponentially slow relaxation of CFD. Connections between CFD and other observables detecting non-local correlations in the system are discussed.

cond-mat.dis-nn↗

arXiv2022

Unsupervised detection of decoupled subspaces: many-body scars and beyond

Highly excited eigenstates of quantum many-body systems are typically featureless thermal states. Some systems, however, possess a small number of special, low-entanglement eigenstates known as quantum scars. We introduce a quantum-inspired machine learning platform based on a Quantum Variational Autoencoder (QVAE) that detects families of scar states in spectra of many-body systems. Unlike a classical autoencoder, QVAE performs a parametrized unitary operation, allowing us to compress a single eigenstate into a smaller number of qubits. We demonstrate that the autoencoder trained on a scar state is able to detect the whole family of scar states sharing common features with the input state. We identify families of quantum many-body scars in the PXP model beyond the $\mathbb{Z}_2$ and $\mathbb{Z}_3$ families and find dynamically decoupled subspaces in the Hilbert space of disordered, interacting spin ladder model. The possibility of an automatic detection of subspaces of scar states opens new pathways in studies of models with a weak breakdown of ergodicity and fragmented Hilbert spaces.

quant-ph↗

arXiv2019

Determination of Chern numbers with a phase retrieval algorithm

Ultracold atoms in optical lattices form a clean quantum simulator platform which can be utilized to examine topological phenomena and test exotic topological materials. Here we propose an experimental scheme to measure the Chern numbers of two-dimensional multiband topological insulators with bosonic atoms. We show how to extract the topological invariants out of a sequence of time-of-flight images by applying a phase retrieval algorithm to matter waves. We illustrate advantages of using bosonic atoms as well as efficiency and robustness of the method with two prominent examples: the Harper-Hofstadter model with an arbitrary commensurate magnetic flux and the Haldane model on a brick-wall lattice.

cond-mat.quant-gas↗