richardson extrapolation
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2022 ◽  
Vol 3 (1) ◽  
pp. 1-37
Author(s):  
Almudena Carrera Vazquez ◽  
Ralf Hiptmair ◽  
Stefan Woerner

We present a quantum algorithm to solve systems of linear equations of the form Ax = b , where A is a tridiagonal Toeplitz matrix and b results from discretizing an analytic function, with a circuit complexity of O (1/√ε, poly (log κ, log N )), where N denotes the number of equations, ε is the accuracy, and κ the condition number. The repeat-until-success algorithm has to be run O (κ/(1-ε)) times to succeed, leveraging amplitude amplification, and needs to be sampled O (1/ε 2 ) times. Thus, the algorithm achieves an exponential improvement with respect to N over classical methods. In particular, we present efficient oracles for state preparation, Hamiltonian simulation, and a set of observables together with the corresponding error and complexity analyses. As the main result of this work, we show how to use Richardson extrapolation to enhance Hamiltonian simulation, resulting in an implementation of Quantum Phase Estimation (QPE) within the algorithm with 1/√ε circuits that can be run in parallel each with circuit complexity 1/√ ε instead of 1/ε. Furthermore, we analyze necessary conditions for the overall algorithm to achieve an exponential speedup compared to classical methods. Our approach is not limited to the considered setting and can be applied to more general problems where Hamiltonian simulation is approximated via product formulae, although our theoretical results would need to be extended accordingly. All the procedures presented are implemented with Qiskit and tested for small systems using classical simulation as well as using real quantum devices available through the IBM Quantum Experience.


Author(s):  
Zahari Zlatev ◽  
Ivan Dimov ◽  
István Faragó ◽  
Krassimir Georgiev ◽  
Ágnes Havasi

AbstractThe numerical treatment of an atmospheric chemical scheme, which contains 56 species, is discussed in this paper. This scheme is often used in studies of air pollution levels in different domains, as, for example, in Europe, by large-scale environmental models containing additionally two other important physical processes—transport of pollutants in the atmosphere (advection) and diffusion phenomena. We shall concentrate our attention on the efficient numerical treatment of the chemical scheme by using Implicit Runge–Kutta Methods combined with accurate and efficient advanced versions of the Richardson Extrapolation. A Variable Stepsize Variable Formula Method is developed in order to achieve high accuracy of the calculated results within a reasonable computational time. Reliable estimations of the computational errors when the proposed numerical methods are used in the treatment of the chemical scheme will be demonstrated by presenting results from several representative runs and comparing these results with “exact” concentrations obtained by applying a very small stepsize during the computations. Results related to the diurnal variations of some of the chemical species will also be presented. The approach used in this paper does not depend on the particular chemical scheme and can easily be applied when other atmospheric chemical schemes are selected.


2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Mesfin Mekuria Woldaregay ◽  
Gemechis File Duressa

Abstract Objectives Numerical treatment of singularly perturbed parabolic delay differential equation is considered. Solution of the equation exhibits a boundary layer, which makes it difficult for numerical computation. Accurate numerical scheme is proposed using $$\theta$$ θ -method in time discretization and non-standard finite difference method in space discretization. Result Stability and uniform convergence of the proposed scheme is investigated. The scheme is uniformly convergent with linear order of convergence before Richardson extrapolation and second order convergent after Richardson extrapolation. Numerical examples are considered to validate the theoretical findings.


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