arXiv subjects

Marcin Płodzień

Publications and source records attributed to Marcin Płodzień.

At least 37 records · Page 2Linked to original sources

arXiv2025

Quantifying superluminal signalling in Schrödinger-Newton model

The Schrödinger-Newton equation aims at describing the dynamics of massive quantum systems subject to the gravitational self-interaction. As a deterministic nonlinear quantum wave equation, it is generally believed to conflict with the relativistic no-signalling principle. Here we challenge this viewpoint and show that it is of key importance to study the quantitative and operational character of the superluminal effects. To this end we employ a rigorous formalism of probability measures on spacetime and quantify the probability of a successful superluminal bit transfer via the single-particle Schrödinger-Newton equation. We demonstrate that such a quantity decreases with the increasing size and mass of the system. Furthermore, we prove that the Einstein-Dirac system, which yields the Schrödinger-Newton equation in the non-relativistic limit, is perfectly compatible with the relativistic causal structure. Our study demonstrates that the Schrödinger-Newton equation, which is by construction non-relativistic, is in fact `more compatible' with the no-signalling principle than the ordinary free Schrödinger equation.

quant-ph↗

arXiv2025

Many-body quantum resources of graph states

Characterizing the non-classical correlations of a complex many-body system is an important part of quantum technologies. A versatile tool for such a task is one that scales well with the size of the system and which can be both easily computed and measured. In this work we focus on graph states, that are promising platforms for quantum computation, simulation and metrology. We consider four topologies, namely the star graph states with edges, Turán graphs, $r$-ary tree graphs, and square grid cluster states, and provide a method to characterise their quantum content: the many-body Bell correlations, non-separability and entanglement depth for an arbitrary number of qubits. We also relate the strength of these many-body correlations to the usefulness of graph states for quantum sensing. Finally, we characterize many-body entanglement depth in graph states with up to $8$ qubits in $146$ classes non-equivalent under local transformations and graph isomorphisms. The technique presented is simple and does not make any assumptions about the multi-qubit state, so it could find applications wherever precise knowledge of many-body quantum correlations is required.

quant-ph↗

arXiv2025

Transfer of quantum-enhanced information through a many-body system

Forthcoming quantum devices will require high-fidelity information transfer across a many-body system. We formulate the criterion for lossless signal propagation and show that a single qubit can play the role of an antenna, collecting large amounts of information from a complex system. We derive the condition under which the antenna, far from the source and embedded in a many-body interacting medium, can still collect the complete information. A striking feature of this setup is that a single qubit antenna can receive even the full signal amplified by the entanglement of the source. As a consequence, the recovery of this information can be performed with simple single-qubit operations on the antenna (which we fully characterize) rather than with multi-qubit measurements of the source. Finally, we discuss the control of the system parameters necessary for lossless signal propagation. A method discussed here could improve the precision of quantum devices and simplify metrological protocols.

quant-ph↗

arXiv2025

Lecture Notes on Information Scrambling, Quantum Chaos, and Haar-Random States

Information scrambling, the process by which quantum information spreads and becomes effectively inaccessible, is central to modern quantum statistical physics and quantum chaos. These lecture notes provide an introduction to information scrambling from both static and dynamical perspectives. The spectral properties of reduced density matrices arising from Haar-random states are developed through the geometry of the unitary group and the universal results of random matrix theory. This geometric framework yields universal, model-independent predictions for entanglement and spectral statistics, capturing generic features of quantum chaos without reference to microscopic details. Dynamical diagnostics such as the spectral form factor and out-of-time-ordered correlators further reveal the onset of chaos in time-dependent evolution. The notes are aimed at advanced undergraduate and graduate students in physics, mathematics, and computer science who are interested in the connections between quantum chaos, information dynamics, and quantum computing. Concepts such as entanglement growth, Haar randomness, random-matrix statistics, and unitary t-designs are introduced through their realization in random quantum circuits and circuit complexity. The same mathematical framework also underpins modern quantum-device benchmarking, where approximate unitary designs and random circuits translate geometric ideas into quantitative tools for assessing fidelity, noise, and scrambling efficiency in real quantum processors.

quant-ph↗

arXiv2025

Boosting thermalization of classical and quantum many-body systems

Understanding and optimizing the relaxation dynamics of many-body systems is essential both for foundational studies in quantum thermodynamics and for applications such as quantum simulation and quantum computing. Efficient preparation of thermal states of a many-body Hamiltonian is governed by the spectral properties of the associated Lindbladian, in particular its spectral gap, which determines the slowest relaxation rate. In this work, we develop a systematic framework for constructing Lindbladians that prepare thermal states. Our approach reveals a simple relation between the relaxation dynamics at finite and infinite temperatures. The framework is scalable to larger system sizes when implemented using tensor-network methods. We find that efficient thermalization requires that the relaxation dynamics respect the symmetries of the thermal state, which reduces the number of free parameters. By applying gradient-based optimization to the Lindbladians, we enhance the spectral gap and thereby boost thermalization. When applied to both classical and quantum spin models, our method demonstrates a substantial enhancement of the spectral gap. For larger system sizes, our approach provides a variational upper bound and enables a certified lower bound on the minimum relaxation rate.

quant-ph↗

arXiv2025

Quantum Machine Learning in Multi-Qubit Phase-Space Part I: Foundations

Quantum machine learning (QML) seeks to exploit the intrinsic properties of quantum mechanical systems, including superposition, coherence, and quantum entanglement for classical data processing. However, due to the exponential growth of the Hilbert space, QML faces practical limits in classical simulations with the state-vector representation of quantum system. On the other hand, phase-space methods offer an alternative by encoding quantum states as quasi-probability functions. Building on prior work in qubit phase-space and the Stratonovich-Weyl (SW) correspondence, we construct a closed, composable dynamical formalism for one- and many-qubit systems in phase-space. This formalism replaces the operator algebra of the Pauli group with function dynamics on symplectic manifolds, and recasts the curse of dimensionality in terms of harmonic support on a domain that scales linearly with the number of qubits. It opens a new route for QML based on variational modelling over phase-space.

quant-ph↗

arXiv2025

Deep Learning in Classical and Quantum Physics

Scientific progress is tightly coupled to the emergence of new research tools. Today, machine learning (ML)-especially deep learning (DL)-has become a transformative instrument for quantum science and technology. Owing to the intrinsic complexity of quantum systems, DL enables efficient exploration of large parameter spaces, extraction of patterns from experimental data, and data-driven guidance for research directions. These capabilities already support tasks such as refining quantum control protocols and accelerating the discovery of materials with targeted quantum properties, making ML/DL literacy an essential skill for the next generation of quantum scientists. At the same time, DL's power brings risks: models can overfit noisy data, obscure causal structure, and yield results with limited physical interpretability. Recognizing these limitations and deploying mitigation strategies is crucial for scientific rigor. These lecture notes provide a comprehensive, graduate-level introduction to DL for quantum applications, combining conceptual exposition with hands-on examples. Organized as a progressive sequence, they aim to equip readers to decide when and how to apply DL effectively, to understand its practical constraints, and to adapt AI methods responsibly to problems across quantum physics, chemistry, and engineering.

quant-ph↗

arXiv2025

Topological quantum thermometry

An optimal local quantum thermometer is a quantum many-body system that saturates the fundamental lower bound for the thermal state temperature estimation accuracy [L. Correa, et. al., Phys. Rev. Lett. 114, 220405 (2015)]. Such a thermometer has a particular energy level structure with a single ground state and highly degenerated excited states manifold, with an energy gap proportional to the estimated temperature. In this work, we show that the optimal local quantum thermometer can be realized in an experimentally feasible system of spinless fermions confined in a one-dimensional optical lattice described by the Rice-Mele model. We characterize the system's sensitivity to temperature changes in terms of quantum Fisher information and the classical Fisher information obtained from experimentally available site occupation measurements.

quant-ph↗

arXiv2025

String breaking dynamics in Ising chain with local vibrations

We consider the dynamics in the one-dimensional quantum Ising model in which each spin coherently interacts with its phononic mode. The model is motivated by quantum simulators based on Rydberg atoms in tweezers or trapped ions. The configuration of two domain walls simulates the particle-antiparticle connecting string. We concentrate on the effect the local vibrations have on the dynamics of this initial state. Our study supplements recent investigations of string breaking, traditionally studied within quantum chromodynamics (QCD), to quantum many-body systems. Two regimes are identified depending on the strength of the coupling with local vibrations. For weak coupling, the string breaking is slowed down as compared to the dynamics in an isolated Ising string. The strong coupling leads to complicated dynamics in which the domain wall character of excitation is dissolved among many coupled states.

quant-ph↗

arXiv2025

Quantics Tensor Train for solving Gross-Pitaevskii equation

We present a quantum-inspired solver for the one-dimensional Gross-Pitaevskii equation in the Quantics Tensor-Train (QTT) representation. By evolving the system entirely within a low-rank tensor manifold, the method sidesteps the memory and runtime barriers that limit conventional finite-difference and spectral schemes. Two complementary algorithms are developed: an imaginary-time projector that drives the condensate toward its variational ground state and a rank-adapted fourth-order Runge-Kutta integrator for real-time dynamics. The framework captures a broad range of physical scenarios - including barrier-confined condensates, quasi-random potentials, long-range dipolar interactions, and multicomponent spinor dynamics - without leaving the compressed representation. Relative to standard discretizations, the QTT approach achieves an exponential reduction in computational resources while retaining quantitative accuracy, thereby extending the practicable regime of Gross-Pitaevskii simulations on classical hardware. These results position tensor networks as a practical bridge between high-performance classical computing and prospective quantum hardware for the numerical treatment of nonlinear Schrodinger-type partial differential equations.

cond-mat.quant-gas↗

arXiv2025

Modern applications of machine learning in quantum sciences

In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning.

quant-ph↗

arXiv2025

Quantum Algorithms for Inverse Participation Ratio Estimation in multi-qubit and multi-qudit systems

Inverse Participation Ratios (IPRs) and the related Participation Entropies quantify the spread of a quantum state over a selected basis of the Hilbert space, offering insights into the equilibrium and non-equilibrium properties of the system. In this work, we propose three quantum algorithms to estimate IPRs on multi-qubit and multi-qudit quantum devices. The first algorithm allows for the estimation of IPRs in the computational basis by single-qubit measurements, while the second one enables measurement of IPR in the eigenbasis of a selected Hamiltonian, without the knowledge about the eigenstates of the system. Next, we provide an algorithm for IPR in the computational basis for a multi-qudit system. We discuss resources required by the algorithms and benchmark them by investigating the one-axis twisting protocol, the thermalization in a deformed PXP model, and the ground state of a spin-$1$ AKLT chain in a transverse field.

quant-ph↗

arXiv2025

Sonification of entanglement dynamics in many-qubit systems

Quantum mechanics poses significant challenges for audio-visual representation, particularly concerning quantum entanglement. Sonification -- the auditory representation of data -- offers a promising complementary approach. This paper investigates sonification techniques applied to dynamical entanglement generation in many-qubit systems with the help of phase space methods and entanglement measure. We study dynamics of entanglement generation in many-qubit system in dynamical protocol governed by two models: the one-axis twisting model, and a quantum kicked-rotor exhibiting both regular and quantum chaotic behavior. We present a procedure of entanglement dynamics sonification, allowing mapping the phase-space representation of a many-qubit quantum state and von Neuman entanglement entropy to sound. Results demonstrate how sonification enhances perception of dynamic entanglement offering intuitive and artistic insight into quantum correlations behaviors.

quant-ph↗

arXiv2025

Quantum Circuits for High-Dimensional Absolutely Maximally Entangled States

Absolutely maximally entangled (AME) states of multipartite quantum systems exhibit maximal entanglement across all possible bipartitions. These states lead to teleportation protocols that surpass standard teleportation schemes, determine quantum error correction codes and can be used to test performance of current term quantum processors. Several AME states can be constructed from graph states using minimal quantum resources. However, there exist other constructions that depart from the stabilizer formalism. In this work, we present explicit quantum circuits to generate exemplary non-stabilizer AME states of four subsystems with four, six, and eight levels each and analyze their capabilities to perform quantum information tasks.

quant-ph↗

arXiv2025

Inherent quantum resources in stationary spin chains

The standard way to generate many-body quantum correlations is via a dynamical protocol: an initial product state is transformed by interactions that generate non-classical correlations at later times. Here, we show that many-body Bell correlations are inherently present in the eigenstates of a variety of spin-1/2 chains. In particular, we show that the eigenstates and thermal states of the collective Lipkin-Meshkov-Glick model possess many-body Bell correlations. We demonstrate that the Bell correlations can take on quantized values that change discontinuously with variations in the total magnetization. Finally, we show that these many-body Bell correlations persist even in the presence of both diagonal and off-diagonal disorder.

quant-ph↗

arXiv2024

Harnessing spin-qubit decoherence to probe strongly-interacting quantum systems

Extracting information from quantum many-body systems remains a key challenge in quantum technologies due to experimental limitations. In this work, we employ a single spin qubit to probe a strongly interacting system, creating an environment conducive to qubit decoherence. By focusing on the XXZ spin chain, we observe diverse dynamics in the qubit evolution, reflecting different parameters of the chain. This demonstrates that a spin qubit can probe both quantitative properties of the spin chain and qualitative characteristics, such as the bipartite entanglement entropy, phase transitions, and perturbation propagation velocity within the system. This approach reveals the power of small quantum systems to probe the properties of large, strongly correlated quantum systems.

quant-ph↗

arXiv2024

Entanglement classification and \emph{non-k}-separability certification via Greenberger-Horne-Zeilinger-class fidelity

Many-body quantum systems can be characterised using the notions of \emph{k}-separability and entanglement depth. A quantum state is \emph{k}-separable if it can be expressed as a mixture of \emph{k} entangled subsystems, and its entanglement depth is given by the size of the largest entangled subsystem. In this paper we propose a multipartite entanglement measure that satisfies the following criteria: (i) it can be used with both pure and mixed states; (ii) it is encoded in a single element of the density matrix, so it does not require knowledge of the full spectrum of the density matrix; (iii) it can be applied to large systems; and (iv) it can be experimentally verified. The proposed method allows the certification of \emph{non-k}-separability of a given quantum state. We show that the proposed method successfully classifies three-qubit systems into known stochastic local operations and classical communication (SLOCC) classes, namely bipartite, \mbox{W-,} and GHZ-type entanglement. Furthermore, we characterise the \emph{non-k}-separability in known nine SLOCC classes of four-qubit states, absolutely maximally entangled states for five and six qubits and for arbitrary size qubit Dicke states.

quant-ph↗

arXiv2024

Enhancing quantum state tomography via resource-efficient attention-based neural networks

Resource-efficient quantum state tomography is one of the key ingredients of future quantum technologies. In this work, we propose a new tomography protocol combining standard quantum state reconstruction methods with an attention-based neural network architecture. We show how the proposed protocol is able to improve the averaged fidelity reconstruction over linear inversion and maximum-likelihood estimation in the finite-statistics regime, reducing at least by an order of magnitude the amount of necessary training data. We demonstrate the potential use of our protocol in physically relevant scenarios, in particular, to certify metrological resources in the form of many-body entanglement generated during the spin squeezing protocols. This could be implemented with the current quantum simulator platforms, such as trapped ions, and ultra-cold atoms in optical lattices.

quant-ph↗