Efficient Pseudomode Representation and Complexity of Quantum Impurity Models
- URL: http://arxiv.org/abs/2409.08816v1
- Date: Fri, 13 Sep 2024 13:31:53 GMT
- Title: Efficient Pseudomode Representation and Complexity of Quantum Impurity Models
- Authors: Julian Thoenniss, Ilya Vilkoviskiy, Dmitry A. Abanin,
- Abstract summary: Out-of-equilibrium fermionic quantum impurity models (QIM) describe a small interacting system coupled to a continuous fermionic bath.
We find efficient bath representations as that of approximating a kernel of the bath's Feynman-Vernon influence functional by a sum of complex exponentials.
To relate our findings to QIM, we derive an explicit Liouvillian that describes the time evolution of the combined impurity-pseudomodes system.
- Score: 0.7373617024876725
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Out-of-equilibrium fermionic quantum impurity models (QIM), describing a small interacting system coupled to a continuous fermionic bath, play an important role in condensed matter physics. Solving such models is a computationally demanding task, and a variety of computational approaches are based on finding approximate representations of the bath by a finite number of modes. In this paper, we formulate the problem of finding efficient bath representations as that of approximating a kernel of the bath's Feynman-Vernon influence functional by a sum of complex exponentials, with each term defining a fermionic pseudomode. Under mild assumptions on the analytic properties of the bath spectral density, we provide an analytic construction of pseudomodes, and prove that their number scales polylogarithmically with the maximum evolution time $T$ and the approximation error $\varepsilon$. We then demonstrate that the number of pseudomodes can be significantly reduced by an interpolative matrix decomposition (ID). Furthermore, we present a complementary approach, based on constructing rational approximations of the bath's spectral density using the ``AAA'' algorithm, followed by compression with ID. The combination of two approaches yields a pseudomode count scaling as $N_\text{ID} \sim \log(T)\log(1/\varepsilon)$, and the agreement between the two approches suggests that the result is close to optimal. Finally, to relate our findings to QIM, we derive an explicit Liouvillian that describes the time evolution of the combined impurity-pseudomodes system. These results establish bounds on the computational resources required for solving out-of-equilibrium QIMs, providing an efficient starting point for tensor-network methods for QIMs.
Related papers
- Noise-Free Sampling Algorithms via Regularized Wasserstein Proximals [3.4240632942024685]
We consider the problem of sampling from a distribution governed by a potential function.
This work proposes an explicit score based MCMC method that is deterministic, resulting in a deterministic evolution for particles.
arXiv Detail & Related papers (2023-08-28T23:51:33Z) - A bound on approximating non-Markovian dynamics by tensor networks in
the time domain [0.9790236766474201]
We show that the spin-boson model can be efficiently simulated using in time computational resources.
This bound indicates that the spin-boson model can be efficiently simulated using in time computational resources.
arXiv Detail & Related papers (2023-07-28T14:50:53Z) - MESSY Estimation: Maximum-Entropy based Stochastic and Symbolic densitY
Estimation [4.014524824655106]
MESSY estimation is a Maximum-Entropy based Gradient and Symbolic densitY estimation method.
We construct a gradient-based drift-diffusion process that connects samples of the unknown distribution function to a guess symbolic expression.
We find that the addition of a symbolic search for basis functions improves the accuracy of the estimation at a reasonable additional computational cost.
arXiv Detail & Related papers (2023-06-07T03:28:47Z) - Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation [59.45669299295436]
We propose a Monte Carlo PDE solver for training unsupervised neural solvers.
We use the PDEs' probabilistic representation, which regards macroscopic phenomena as ensembles of random particles.
Our experiments on convection-diffusion, Allen-Cahn, and Navier-Stokes equations demonstrate significant improvements in accuracy and efficiency.
arXiv Detail & Related papers (2023-02-10T08:05:19Z) - Efficient low temperature simulations for fermionic reservoirs with the
hierarchical equations of motion method: Application to the Anderson impurity
model [0.0]
In this work, we employ the barycentric representation to approximate the Fermi function and hybridization functions in the frequency domain.
The new method, by approxing these functions with optimized rational decomposition, greatly reduces the number of basis functions in decomposing the reservoir correlation functions.
We demonstrate the efficiency, accuracy, and long-time stability of the new decomposition scheme by applying it to the Anderson impurity model (AIM) in the low-temperature regime.
arXiv Detail & Related papers (2022-11-08T08:46:23Z) - Sampling with Mollified Interaction Energy Descent [57.00583139477843]
We present a new optimization-based method for sampling called mollified interaction energy descent (MIED)
MIED minimizes a new class of energies on probability measures called mollified interaction energies (MIEs)
We show experimentally that for unconstrained sampling problems our algorithm performs on par with existing particle-based algorithms like SVGD.
arXiv Detail & Related papers (2022-10-24T16:54:18Z) - A blob method method for inhomogeneous diffusion with applications to
multi-agent control and sampling [0.6562256987706128]
We develop a deterministic particle method for the weighted porous medium equation (WPME) and prove its convergence on bounded time intervals.
Our method has natural applications to multi-agent coverage algorithms and sampling probability measures.
arXiv Detail & Related papers (2022-02-25T19:49:05Z) - Mean-Square Analysis with An Application to Optimal Dimension Dependence
of Langevin Monte Carlo [60.785586069299356]
This work provides a general framework for the non-asymotic analysis of sampling error in 2-Wasserstein distance.
Our theoretical analysis is further validated by numerical experiments.
arXiv Detail & Related papers (2021-09-08T18:00:05Z) - Hybridized Methods for Quantum Simulation in the Interaction Picture [69.02115180674885]
We provide a framework that allows different simulation methods to be hybridized and thereby improve performance for interaction picture simulations.
Physical applications of these hybridized methods yield a gate complexity scaling as $log2 Lambda$ in the electric cutoff.
For the general problem of Hamiltonian simulation subject to dynamical constraints, these methods yield a query complexity independent of the penalty parameter $lambda$ used to impose an energy cost.
arXiv Detail & Related papers (2021-09-07T20:01:22Z) - 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) - Fast approximations in the homogeneous Ising model for use in scene
analysis [61.0951285821105]
We provide accurate approximations that make it possible to numerically calculate quantities needed in inference.
We show that our approximation formulae are scalable and unfazed by the size of the Markov Random Field.
The practical import of our approximation formulae is illustrated in performing Bayesian inference in a functional Magnetic Resonance Imaging activation detection experiment, and also in likelihood ratio testing for anisotropy in the spatial patterns of yearly increases in pistachio tree yields.
arXiv Detail & Related papers (2017-12-06T14:24:34Z)
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.