Tangent equations of motion for nonlinear response functions
- URL: http://arxiv.org/abs/2603.22137v1
- Date: Mon, 23 Mar 2026 16:00:00 GMT
- Title: Tangent equations of motion for nonlinear response functions
- Abstract summary: We establish a framework to compute nonlinear response functions directly from real-time dynamics.<n>Our approach is based on the Gateaux derivative with respect to the external field in function space.<n>We demonstrate the power of TEOM by computing frequency-resolved fifth-order response functions for a solid-state electron model.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Nonlinear response functions, formulated as multipoint correlation functions or Volterra kernels, encode the dynamical and spectroscopic properties of physical systems and underpin a wide range of nonlinear transport and optical phenomena. However, their evaluation rapidly becomes prohibitive at high orders because of combinatorial (often factorial) scaling or severe numerical errors. Here, we establish a systematic and efficient framework to compute nonlinear response functions directly from real-time dynamics, without explicitly constructing multipoint correlators or relying on numerically unstable finite-difference methods for order-resolved extraction. Our approach is based on the Gateaux derivative with respect to the external field in function space, which yields a closed hierarchy of tangent equations of motion (TEOM). Propagating the TEOM alongside the original dynamics isolates each perturbative order with high accuracy, providing a term-by-term decomposition of physical contributions. The computational cost scales exponentially with response order in the fully general setting and reduces to polynomial complexity when all perturbation directions are identical; both regimes avoid the factorial scaling of explicit multipoint-correlator evaluations. We demonstrate the power of TEOM by computing frequency-resolved fifth-order response functions for a solid-state electron model and by obtaining nonlinear response functions up to the 49th order with controlled accuracy in a classical Duffing oscillator. We further show that our time-evolution formulation allows optical conductivities to be evaluated directly while remaining numerically stable even near zero frequency. TEOM can be incorporated seamlessly into existing real-time evolution methods, yielding a general framework for computing nonlinear response functions in quantum and classical dynamical systems.
Related papers
- A General First- and Second-Order Numerical Solver for Non-Markovian Quantum State Diffusion [0.0]
We present a general auxiliary-state framework for arbitrary bath correlation functions.<n>We then construct first- and second-order schemes and provide diagrammatic transition rules for their explicit implementation.<n>Results verify the expected temporal accuracy and demonstrate the applicability of the proposed methods to different bath correlation functions and multi-level quantum systems.
arXiv Detail & Related papers (2026-07-28T08:24:21Z) - Data-driven forced response analysis with min-max representations of nonlinear restoring forces [0.0]
This paper discusses a novel data-driven nonlinearity identification method for mechanical systems with nonlinear restoring forces.<n>The proposed method is built upon the universal approximation theorem that states that a nonlinear function can be approximated by a linear combination of activation functions.
arXiv Detail & Related papers (2026-03-17T16:23:33Z) - Disordered Dynamics in High Dimensions: Connections to Random Matrices and Machine Learning [52.26396748560348]
We provide an overview of high dimensional dynamical systems driven by random matrices.<n>We focus on applications to simple models of learning and generalization in machine learning theory.
arXiv Detail & Related papers (2026-01-03T00:12:32Z) - Quantum Regression Theory and Efficient Computation of Response Functions for Non-Markovian Open Systems [6.8865259790065005]
We develop a memoryless, system-only formulation of two-point correlations for open quantum systems.<n>We recast the total response function into evolutions generated by time-dependent Hamiltonian and Lindblad primitives.<n>We present quantum algorithms for these primitives and obtain an estimator for two-time correlations whose cost scales poly-logarithmically.
arXiv Detail & Related papers (2025-10-07T00:23:33Z) - Extracting Nonlinear Dynamical Response Functions from Time Evolution [0.0]
We develop a general framework based on the functional derivative to extract nonlinear dynamical response functions from the temporal evolution of physical quantities.<n>We validate our approach by calculating the second- and third-order optical responses in the Rice-Mele model.<n>This framework is broadly applicable to any method that can compute real-time dynamics, offering a powerful and versatile tool for investigating nonlinear responses in dynamical systems.
arXiv Detail & Related papers (2025-07-10T12:00:00Z) - Imaginary Time Formalism for Causal Nonlinear Response Functions [0.0]
We show that casual nonlinear response functions at every order can be obtained from an analytic continuation of an appropriate time-ordered Matsubara function.<n>We also use our result to find an analytic spectral density representation for both causal response functions and Matsubara functions.
arXiv Detail & Related papers (2025-06-26T16:11:05Z) - Generative System Dynamics in Recurrent Neural Networks [56.958984970518564]
We investigate the continuous time dynamics of Recurrent Neural Networks (RNNs)<n>We show that skew-symmetric weight matrices are fundamental to enable stable limit cycles in both linear and nonlinear configurations.<n> Numerical simulations showcase how nonlinear activation functions not only maintain limit cycles, but also enhance the numerical stability of the system integration process.
arXiv Detail & Related papers (2025-04-16T10:39:43Z) - Nonlinear Neural Dynamics and Classification Accuracy in Reservoir Computing [3.196204482566275]
We study the accuracy of a reservoir computer in artificial classification tasks of varying complexity.
We find that, even for activation functions with extremely reduced nonlinearity, weak recurrent interactions and small input signals, the reservoir is able to compute useful representations.
arXiv Detail & Related papers (2024-11-15T08:52:12Z) - Neural Pfaffians: Solving Many Many-Electron Schrödinger Equations [58.130170155147205]
Neural wave functions accomplished unprecedented accuracies in approximating the ground state of many-electron systems, though at a high computational cost.
Recent works proposed amortizing the cost by learning generalized wave functions across different structures and compounds instead of solving each problem independently.
This work tackles the problem by defining overparametrized, fully learnable neural wave functions suitable for generalization across molecules.
arXiv Detail & Related papers (2024-05-23T16:30:51Z) - 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) - Designing Kerr Interactions for Quantum Information Processing via
Counterrotating Terms of Asymmetric Josephson-Junction Loops [68.8204255655161]
static cavity nonlinearities typically limit the performance of bosonic quantum error-correcting codes.
Treating the nonlinearity as a perturbation, we derive effective Hamiltonians using the Schrieffer-Wolff transformation.
Results show that a cubic interaction allows to increase the effective rates of both linear and nonlinear operations.
arXiv Detail & Related papers (2021-07-14T15:11:05Z) - 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.