A graph-theoretic approach to chaos and complexity in quantum systems
- URL: http://arxiv.org/abs/2502.16404v2
- Date: Tue, 22 Apr 2025 12:02:29 GMT
- Title: A graph-theoretic approach to chaos and complexity in quantum systems
- Abstract summary: We explore, via the commutator graph, average notions of scrambling, chaos and complexity over ensembles of systems with Lie algebras.<n>We link graph-theoretic properties of the commutator graph to the out-of-time-orderor (OTOC), the frame potential, the frustration graph of the Hamiltonian of the system, and the Krylov complexity of operators evolving under the dynamics.
- Score: 0.4934360430803066
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: There has recently been considerable interest in studying quantum systems via dynamical Lie algebras (DLAs) -- Lie algebras generated by the terms which appear in the Hamiltonian of the system. However, there are some important properties that are revealed only at a finer level of granularity than the DLA. In this work we explore, via the commutator graph, average notions of scrambling, chaos and complexity over ensembles of systems with DLAs that possess a basis consisting of Pauli strings. Unlike DLAs, commutator graphs are sensitive to short-time dynamics, and therefore constitute a finer probe to various characteristics of the corresponding ensemble. We link graph-theoretic properties of the commutator graph to the out-of-time-order correlator (OTOC), the frame potential, the frustration graph of the Hamiltonian of the system, and the Krylov complexity of operators evolving under the dynamics. For example, we reduce the calculation of average OTOCs to a counting problem on the graph; separately, we connect the Krylov complexity of an operator to the module structure of the adjoint action of the DLA on the space of operators in which it resides, and prove that its average over the ensemble is lower bounded by the average shortest path length between the initial operator and the other operators in the commutator graph.
Related papers
- Topological Control of Quantum Chaos Diagnostics: OTOCs, Spectral Statistics, and Information Scrambling in Ising Model [0.0]
We investigate the integrability-to-chaos transition and information scrambling in Ising spin networks.<n>Long-range couplings and heterogeneous degree distributions drastically accelerate quantum information propagation.<n>This work establishes a unified framework bridging network topology with information-theoretic, operator, and spectral diagnostics.
arXiv Detail & Related papers (2026-07-02T17:29:35Z) - Directed Graph Topology Inference via Graph Filter Identification [54.541275287889164]
We address the problem of inferring a directed network from nodal measurements generated by a graph convolutional filter.<n>A joint graph filter and topology identification algorithm is also proposed, which alternates between the aforementioned steps to offer improved sample complexity.
arXiv Detail & Related papers (2026-06-25T18:25:57Z) - Sector length distributions of recursively definable graph states through analytic combinatorics [0.0]
We encode a family of quantum state's SLD as a generating function.<n>We find expressions for graph states' concentratable entanglement, bounds on their depolarizing fidelity, and a multipartite entanglement.
arXiv Detail & Related papers (2026-04-10T18:00:01Z) - Logarithmic growth of operator entanglement in a clean non-integrable circuit [0.0]
We study intermediate and long-time dynamics of a system in a finite, large volume.<n>Despite the model being non-integrable and free from any quenched disorder, the operator entanglement grows at most logarithmic in time.<n>The operator size distribution also becomes bimodal at certain times, displaying intermediate behavior between chaotic and free systems.
arXiv Detail & Related papers (2026-03-19T18:01:34Z) - Long-Range Pairing in the Kitaev Model: Krylov Subspace Signatures [0.0]
We show that Lanczos coefficients generated from local boundary operators provide a quantitative diagnostic of excitation gap.<n>We derive an exact single-particle operator Lanczos algorithm that reduces the recursion from exponentially large operator space to a finite-dimensional linear problem.<n>These results establish Krylov diagnostics as operational probes of how low-energy excitations are localized along the chain.
arXiv Detail & Related papers (2026-02-11T19:00:05Z) - Transformers Provably Learn Directed Acyclic Graphs via Kernel-Guided Mutual Information [91.66597637613263]
transformer-based models leveraging the attention mechanism have demonstrated strong empirical success in capturing complex dependencies within graphs.<n>We introduce a novel information-theoretic metric: the kernel-guided mutual information (KG-MI) based on the $f$-divergence.<n>We prove that, given sequences generated by a $K$-parent DAG, training a single-layer, multi-head transformer via a gradient ascent converges to the global optimum time.
arXiv Detail & Related papers (2025-10-29T14:07:12Z) - On the dynamical Lie algebras of quantum approximate optimization algorithms [4.987686869768721]
Dynamical Lie algebras (DLAs) have emerged as a valuable tool in the study of parameterized quantum circuits.
In this work, we investigate DLAs for the quantum approximate optimization algorithm (QAOA)
We show that the dimension of the DLA is $O(n3)$ and give an explicit basis for the DLA.
arXiv Detail & Related papers (2024-07-17T14:12:30Z) - KPZ scaling from the Krylov space [83.88591755871734]
Recently, a superdiffusion exhibiting the Kardar-Parisi-Zhang scaling in late-time correlators and autocorrelators has been reported.
Inspired by these results, we explore the KPZ scaling in correlation functions using their realization in the Krylov operator basis.
arXiv Detail & Related papers (2024-06-04T20:57:59Z) - LSEnet: Lorentz Structural Entropy Neural Network for Deep Graph Clustering [59.89626219328127]
Graph clustering is a fundamental problem in machine learning.
Deep learning methods achieve the state-of-the-art results in recent years, but they still cannot work without predefined cluster numbers.
We propose to address this problem from a fresh perspective of graph information theory.
arXiv Detail & Related papers (2024-05-20T05:46:41Z) - Operator dynamics in Lindbladian SYK: a Krylov complexity perspective [0.0]
We analytically establish the linear growth of two sets of coefficients for any generic jump operators.
We find that the Krylov complexity saturates inversely with the dissipation strength, while the dissipative timescale grows logarithmically.
arXiv Detail & Related papers (2023-11-01T18:00:06Z) - Operational Quantum Mereology and Minimal Scrambling [3.499870393443268]
We will attempt to answer what are the natural quantum subsystems which emerge out of a system's dynamical laws.
We first define generalized tensor product structures (gTPS) in terms of observables, as dual pairs of an operator subalgebra $cal A$ and its commutant.
We propose an operational criterion of minimal information scrambling at short time scales to dynamically select gTPS.
arXiv Detail & Related papers (2022-12-29T15:14:38Z) - Scrambling and quantum chaos indicators from long-time properties of
operator distributions [0.0]
Scrambling is a key concept in the analysis of nonequilibrium properties of quantum many-body systems.
We study the structure of the expansion coefficients treated as a coarse-grained probability distribution in the space of operators.
We show that the long-time properties of the operator distribution display common features across these cases.
arXiv Detail & Related papers (2022-11-29T02:06:30Z) - Decimation technique for open quantum systems: a case study with
driven-dissipative bosonic chains [62.997667081978825]
Unavoidable coupling of quantum systems to external degrees of freedom leads to dissipative (non-unitary) dynamics.
We introduce a method to deal with these systems based on the calculation of (dissipative) lattice Green's function.
We illustrate the power of this method with several examples of driven-dissipative bosonic chains of increasing complexity.
arXiv Detail & Related papers (2022-02-15T19:00:09Z) - Out-of-time-order correlations and the fine structure of eigenstate
thermalisation [58.720142291102135]
Out-of-time-orderors (OTOCs) have become established as a tool to characterise quantum information dynamics and thermalisation.
We show explicitly that the OTOC is indeed a precise tool to explore the fine details of the Eigenstate Thermalisation Hypothesis (ETH)
We provide an estimation of the finite-size scaling of $omega_textrmGOE$ for the general class of observables composed of sums of local operators in the infinite-temperature regime.
arXiv Detail & Related papers (2021-03-01T17:51:46Z) - Relevant OTOC operators: footprints of the classical dynamics [68.8204255655161]
The OTOC-RE theorem relates the OTOCs summed over a complete base of operators to the second Renyi entropy.
We show that the sum over a small set of relevant operators, is enough in order to obtain a very good approximation for the entropy.
In turn, this provides with an alternative natural indicator of complexity, i.e. the scaling of the number of relevant operators with time.
arXiv Detail & Related papers (2020-07-31T19:23:26Z) - Structural Landmarking and Interaction Modelling: on Resolution Dilemmas
in Graph Classification [50.83222170524406]
We study the intrinsic difficulty in graph classification under the unified concept of resolution dilemmas''
We propose SLIM'', an inductive neural network model for Structural Landmarking and Interaction Modelling.
arXiv Detail & Related papers (2020-06-29T01:01:42Z) - Multipole Graph Neural Operator for Parametric Partial Differential
Equations [57.90284928158383]
One of the main challenges in using deep learning-based methods for simulating physical systems is formulating physics-based data.
We propose a novel multi-level graph neural network framework that captures interaction at all ranges with only linear complexity.
Experiments confirm our multi-graph network learns discretization-invariant solution operators to PDEs and can be evaluated in linear time.
arXiv Detail & Related papers (2020-06-16T21:56:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.