On Krylov complexity in open systems: an approach via bi-Lanczos
algorithm
- URL: http://arxiv.org/abs/2303.04175v2
- Date: Thu, 14 Dec 2023 09:36:05 GMT
- Title: On Krylov complexity in open systems: an approach via bi-Lanczos
algorithm
- Authors: Aranya Bhattacharya, Pratik Nandy, Pingal Pratyush Nath, Himanshu Sahu
- Abstract summary: We resort to the bi-Lanczos algorithm generating two bi-orthogonal Krylov spaces, which individually generate non-orthogonal subspaces.
Unlike the previously studied Arnoldi iteration, this algorithm renders the Lindbladian into a purely tridiagonal form.
Our study relies on two specific systems, the dissipative transverse-field Ising model (TFIM) and the dissipative interacting XXZ chain.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Continuing the previous initiatives arXiv: 2207.05347 and arXiv: 2212.06180,
we pursue the exploration of operator growth and Krylov complexity in
dissipative open quantum systems. In this paper, we resort to the bi-Lanczos
algorithm generating two bi-orthogonal Krylov spaces, which individually
generate non-orthogonal subspaces. Unlike the previously studied Arnoldi
iteration, this algorithm renders the Lindbladian into a purely tridiagonal
form, thus opening up a possibility to study a wide class of dissipative
integrable and chaotic systems by computing Krylov complexity at late times.
Our study relies on two specific systems, the dissipative transverse-field
Ising model (TFIM) and the dissipative interacting XXZ chain. We find that, for
the weak coupling, initial Lanczos coefficients can efficiently distinguish
integrable and chaotic evolution before the dissipative effect sets in, which
results in more fluctuations in higher Lanczos coefficients. This results in
the equal saturation of late-time complexity for both integrable and chaotic
cases, making the notion of late-time chaos dubious.
Related papers
- Learning Intersections of Two Margin Halfspaces under Factorizable Distributions [56.51474048985742]
Learning intersections of halfspaces is a central problem in Computational Learning Theory.<n>Even for just two halfspaces, it remains a major open question whether learning is possible in time.<n>We introduce a novel algorithm that provably circumvents the CSQ hardness barrier.
arXiv Detail & Related papers (2025-11-13T00:28:24Z) - Quantum Chaos Diagnostics for Open Quantum Systems from Bi-Lanczos Krylov Dynamics [2.0603431589684518]
In Hermitian systems, Krylov complexity has emerged as a powerful diagnostic of quantum dynamics.<n>Here, we demonstrate that Krylov complexity, computed via the bi-Lanczos algorithm, effectively identifies chaotic and integrable phases in open quantum systems.
arXiv Detail & Related papers (2025-08-19T15:49:09Z) - 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) - 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) - Krylov complexity from integrability to chaos [0.0]
We apply a notion of quantum complexity, called "Krylov complexity", to study the evolution of systems from integrability to chaos.
We investigate the integrable XXZ spin chain, enriched with an integrability breaking deformation that allows one to interpolate between integrable and chaotic behavior.
We find that the chaotic system indeed approaches the RMT behavior in the appropriate symmetry class.
arXiv Detail & Related papers (2022-07-15T18:51:13Z) - First-Order Algorithms for Nonlinear Generalized Nash Equilibrium
Problems [88.58409977434269]
We consider the problem of computing an equilibrium in a class of nonlinear generalized Nash equilibrium problems (NGNEPs)
Our contribution is to provide two simple first-order algorithmic frameworks based on the quadratic penalty method and the augmented Lagrangian method.
We provide nonasymptotic theoretical guarantees for these algorithms.
arXiv Detail & Related papers (2022-04-07T00:11:05Z) - Simultaneous Transport Evolution for Minimax Equilibria on Measures [48.82838283786807]
Min-max optimization problems arise in several key machine learning setups, including adversarial learning and generative modeling.
In this work we focus instead in finding mixed equilibria, and consider the associated lifted problem in the space of probability measures.
By adding entropic regularization, our main result establishes global convergence towards the global equilibrium.
arXiv Detail & Related papers (2022-02-14T02:23:16Z) - Krylov Localization and suppression of complexity [0.0]
We investigate Krylov complexity for the case of interacting integrable models at finite size.
We find that complexity saturation is suppressed as compared to chaotic systems.
We demonstrate this behavior for an interacting integrable model, the XXZ spin chain.
arXiv Detail & Related papers (2021-12-22T18:45:32Z) - Krylov complexity of many-body localization: Operator localization in
Krylov basis [0.0]
We study the operator growth problem and its complexity in the many-body localization (MBL) system from the Lanczos perspective.
Using the Krylov basis, the operator growth problem can be viewed as a single-particle hopping problem on a semi-infinite chain.
Our numerical results suggest that the emergent single-particle hopping problem in the MBL system is localized when on the first site.
arXiv Detail & Related papers (2021-12-09T06:50:19Z) - Lattice-Based Methods Surpass Sum-of-Squares in Clustering [98.46302040220395]
Clustering is a fundamental primitive in unsupervised learning.
Recent work has established lower bounds against the class of low-degree methods.
We show that, perhaps surprisingly, this particular clustering model textitdoes not exhibit a statistical-to-computational gap.
arXiv Detail & Related papers (2021-12-07T18:50:17Z) - Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable
Approach for Continuous Markov Random Fields [53.31927549039624]
We show that a piecewise discretization preserves better contrast from existing discretization problems.
We apply this theory to the problem of matching two images.
arXiv Detail & Related papers (2021-07-13T12:31:06Z) - Operator complexity: a journey to the edge of Krylov space [0.0]
Krylov complexity, or K-complexity', quantifies this growth with respect to a special basis.
We study the evolution of K-complexity in finite-entropy systems for time scales greater than the scrambling time.
arXiv Detail & Related papers (2020-09-03T18:10:20Z) - Single-Timescale Stochastic Nonconvex-Concave Optimization for Smooth
Nonlinear TD Learning [145.54544979467872]
We propose two single-timescale single-loop algorithms that require only one data point each step.
Our results are expressed in a form of simultaneous primal and dual side convergence.
arXiv Detail & Related papers (2020-08-23T20:36:49Z) - The limits of min-max optimization algorithms: convergence to spurious
non-critical sets [82.74514886461257]
min-max optimization algorithms encounter problems far greater because of the existence of periodic cycles and similar phenomena.
We show that there exist algorithms that do not attract any points of the problem.
We illustrate such challenges in simple to almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost almost
arXiv Detail & Related papers (2020-06-16T10:49:27Z)
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.