Fast Algorithms of Bath Calculations in Simulations of Quantum
System-Bath Dynamics
- URL: http://arxiv.org/abs/2202.06190v2
- Date: Fri, 6 May 2022 06:31:49 GMT
- Title: Fast Algorithms of Bath Calculations in Simulations of Quantum
System-Bath Dynamics
- Authors: Zhenning Cai, Jianfeng Lu, Siyao Yang
- Abstract summary: We present fast algorithms for the summation of Dyson series and the inchworm Monte Carlo method for quantum systems.
The algorithms are based on evolving the integro-differential equations where the most expensive part comes from the computation of bath influence functionals.
It is proven that the proposed fast algorithms reduce the number of such calculations by a factor of $O(N)$, where $N$ is the total number of time steps.
- Score: 5.989041429080286
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present fast algorithms for the summation of Dyson series and the inchworm
Monte Carlo method for quantum systems that are coupled with harmonic baths.
The algorithms are based on evolving the integro-differential equations where
the most expensive part comes from the computation of bath influence
functionals. To accelerate the computation, we design fast algorithms based on
reusing the bath influence functionals computed in the previous time steps to
reduce the number of calculations. It is proven that the proposed fast
algorithms reduce the number of such calculations by a factor of $O(N)$, where
$N$ is the total number of time steps. Numerical experiments are carried out to
show the efficiency of the method and to verify the theoretical results.
Related papers
- Faster WIND: Accelerating Iterative Best-of-$N$ Distillation for LLM Alignment [81.84950252537618]
This paper reveals a unified game-theoretic connection between iterative BOND and self-play alignment.
We establish a novel framework, WIN rate Dominance (WIND), with a series of efficient algorithms for regularized win rate dominance optimization.
arXiv Detail & Related papers (2024-10-28T04:47:39Z) - A quantum algorithm for advection-diffusion equation by a probabilistic imaginary-time evolution operator [0.0]
We propose a quantum algorithm for solving the linear advection-diffusion equation by employing a new approximate probabilistic imaginary-time evolution (PITE) operator.
We construct the explicit quantum circuit for realizing the imaginary-time evolution of the Hamiltonian coming from the advection-diffusion equation.
Our algorithm gives comparable result to the Harrow-Hassidim-Lloyd (HHL) algorithm with similar gate complexity, while we need much less ancillary qubits.
arXiv Detail & Related papers (2024-09-27T08:56:21Z) - Solving Fractional Differential Equations on a Quantum Computer: A Variational Approach [0.1492582382799606]
We introduce an efficient variational hybrid quantum-classical algorithm designed for solving Caputo time-fractional partial differential equations.
Our results indicate that solution fidelity is insensitive to the fractional index and that gradient evaluation cost scales economically with the number of time steps.
arXiv Detail & Related papers (2024-06-13T02:27:16Z) - A Constrained BA Algorithm for Rate-Distortion and Distortion-Rate
Functions [13.570794979535934]
modification of Blahut-Arimoto (BA) algorithm for rate-distortion functions.
modified algorithm directly computes the RD function for a given target distortion.
arXiv Detail & Related papers (2023-05-04T08:41:03Z) - Fast Computation of Optimal Transport via Entropy-Regularized Extragradient Methods [75.34939761152587]
Efficient computation of the optimal transport distance between two distributions serves as an algorithm that empowers various applications.
This paper develops a scalable first-order optimization-based method that computes optimal transport to within $varepsilon$ additive accuracy.
arXiv Detail & Related papers (2023-01-30T15:46:39Z) - A Sublinear-Time Quantum Algorithm for Approximating Partition Functions [0.0]
We present a novel quantum algorithm for estimating Gibbs partition functions in sublinear time.
This is the first speed-up of this type to be obtained over the seminal nearly-linear time of vStefankovivc, Vempala and Vigoda.
arXiv Detail & Related papers (2022-07-18T14:41:48Z) - The Bold-Thin-Bold Diagrammatic Monte Carlo Method for Open Quantum
Systems [0.0]
We present two diagrammatic Monte Carlo methods for quantum systems coupled with harmonic baths.
The underlying mechanism of the governing equations associated with the two methods lies in the recurrence relation of the path integrals.
Compared with the algorithms therein, our methods show better performance in terms of computational efficiency and memory cost.
arXiv Detail & Related papers (2022-05-22T18:17:23Z) - Alternatives to a nonhomogeneous partial differential equation quantum
algorithm [52.77024349608834]
We propose a quantum algorithm for solving nonhomogeneous linear partial differential equations of the form $Apsi(textbfr)=f(textbfr)$.
These achievements enable easier experimental implementation of the quantum algorithm based on nowadays technology.
arXiv Detail & Related papers (2022-05-11T14:29:39Z) - Provably Faster Algorithms for Bilevel Optimization [54.83583213812667]
Bilevel optimization has been widely applied in many important machine learning applications.
We propose two new algorithms for bilevel optimization.
We show that both algorithms achieve the complexity of $mathcalO(epsilon-1.5)$, which outperforms all existing algorithms by the order of magnitude.
arXiv Detail & Related papers (2021-06-08T21:05:30Z) - Efficient Computation of Expectations under Spanning Tree Distributions [67.71280539312536]
We propose unified algorithms for the important cases of first-order expectations and second-order expectations in edge-factored, non-projective spanning-tree models.
Our algorithms exploit a fundamental connection between gradients and expectations, which allows us to derive efficient algorithms.
arXiv Detail & Related papers (2020-08-29T14:58:26Z) - Coded Distributed Computing with Partial Recovery [56.08535873173518]
We introduce a novel coded matrix-vector multiplication scheme, called coded computation with partial recovery (CCPR)
CCPR reduces both the computation time and the decoding complexity by allowing a trade-off between the accuracy and the speed of computation.
We then extend this approach to distributed implementation of more general computation tasks by proposing a coded communication scheme with partial recovery.
arXiv Detail & Related papers (2020-07-04T21:34:49Z)
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.