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Quantum error mitigation (QEM) provides a practical route for estimating reliable observables on noisy intermediate-scale quantum (NISQ) devices. Traditional QEM strategies, including zero-noise extrapolation (ZNE) and Clifford data…

Quantum Physics · Physics 2026-04-21 Huaxin Wang , Xinge Wu , Jiajun Liu , Ruiqing He , Jiandong Shang , Hengliang Guo , Qiang Chen

Efficient solvers for tensor eigenvalue problems are important tools for the analysis of higher-order data sets. Here we introduce, analyze and demonstrate an extrapolation method to accelerate the widely used shifted symmetric higher order…

Numerical Analysis · Mathematics 2023-07-25 Sara Pollock , Rhea Shroff

Extrapolation is a well-known technique for solving convex optimization and variational inequalities and recently attracts some attention for non-convex optimization. Several recent works have empirically shown its success in some machine…

Optimization and Control · Mathematics 2019-02-06 Yi Xu , Zhuoning Yuan , Sen Yang , Rong Jin , Tianbao Yang

Quantum error mitigation (QEM) infers noiseless expectation values from noisy variants of a target quantum circuit. Unlike quantum error correction, QEM requires no additional hardware resources and is therefore routinely employed in…

Quantum Physics · Physics 2026-03-04 Raam Uzdin

Recent works have suggested that nonlinear (quadratic) effects in black hole perturbation theory may be important for describing a black hole ringdown. We show that the technique of uniform approximations can be used to accurately compute…

High Energy Physics - Theory · Physics 2023-12-12 Bruno Bucciotti , Adrien Kuntz , Francesco Serra , Enrico Trincherini

Image interpolation based on diffusion models is promising in creating fresh and interesting images. Advanced interpolation methods mainly focus on spherical linear interpolation, where images are encoded into the noise space and then…

Computer Vision and Pattern Recognition · Computer Science 2024-03-15 PengFei Zheng , Yonggang Zhang , Zhen Fang , Tongliang Liu , Defu Lian , Bo Han

We consider frequency estimation in a noisy environment with noisy probes. This builds on previous studies, most of which assume that the initial probe state is pure, while the encoding process is noisy, or that the initial probe state is…

Classical simulations of noisy stabilizer circuits are often used to estimate the threshold of a quantum error-correcting code. Physical noise sources are efficiently approximated by random insertions of Pauli operators. For a single qubit,…

Quantum Physics · Physics 2015-03-05 Mauricio Gutiérrez , Kenneth R. Brown

Peephole optimization of quantum circuits provides a method of leveraging standard circuit synthesis approaches into scalable quantum circuit optimization. One application of this technique partitions an entire circuit into a series of…

Quantum Physics · Physics 2024-09-11 Joseph Clark , Himanshu Thapliyal

Quantum systems are inherently susceptible to noise -- a notorious factor that induces decoherence and limits the performance of quantum applications. To mitigate its detrimental effects, various techniques have been developed, including…

Quantum Physics · Physics 2025-05-21 Yu-Bo Hou , Xiaoan Ai , Ruizhe You , Changchun Zhong

We present two approaches for enhancing the accuracy of second order finite difference approximations of two-dimensional semilinear parabolic systems. These are the fourth order compact difference scheme and the fourth order scheme based on…

Numerical Analysis · Mathematics 2017-01-12 Ivan Dimov , Juri Kandilarov , Venelin Todorov , Lubin Vulkov

Our objective is to calculate the derivatives of data corrupted by noise. This is a challenging task as even small amounts of noise can result in significant errors in the computation. This is mainly due to the randomness of the noise,…

Numerical Analysis · Mathematics 2023-04-13 Phuong M. Nguyen , Thuy T. Le , Loc H. Nguyen , Michael V. Klibanov

Function approximation is a generic process in a variety of computational problems, from data interpolation to the solution of differential equations and inverse problems. In this work, a unified approach for such techniques is…

Numerical Analysis · Mathematics 2019-10-01 Nikolaos P. Bakas

This paper explores the incorporation of Tikhonov regularization into the least squares approximation scheme using trigonometric polynomials on the unit circle. This approach encompasses interpolation and hyperinterpolation as specific…

Numerical Analysis · Mathematics 2025-05-26 Congpei An , Mou Cai

To address noise inherent in electronic data acquisition systems and real world sources, Araki et al. [Physica D: Nonlinear Phenomena, 417 (2021) 132819] demonstrated a grid based nonlinear technique to remove noise from a chaotic signal,…

A number of regularization methods for discrete inverse problems consist in considering weighted versions of the usual least square solution. However, these so-called filter methods are generally restricted to monotonic transformations,…

Statistics Theory · Mathematics 2011-05-05 Paul Rochet

A denoising technique based on noise invalidation is proposed. The adaptive approach derives a noise signature from the noise order statistics and utilizes the signature to denoise the data. The novelty of this approach is in presenting a…

Methodology · Statistics 2015-05-19 Soosan Beheshti , Masoud Hashemi , Xiao-Ping Zhang , Nima Nikvand

A modular method was suggested before to recover a band limited signal from the sample and hold and linearly interpolated (or, in general, an nth-order-hold) version of the regular samples. In this paper a novel approach for compensating…

Multimedia · Computer Science 2010-11-12 Ali Ayremlou , Mohammad Tofighi , Farokh Marvasti

Extrapolation is a generic problem in physics and mathematics: how to use asymptotic data in one parametric regime to learn about the behavior of a function in another parametric regime. For example: extending weak coupling expansions to…

High Energy Physics - Theory · Physics 2019-10-25 Ovidiu Costin , Gerald V. Dunne

This paper aims to investigate the numerical approximation of semilinear non-autonomous stochastic partial differential equations (SPDEs) driven by multiplicative or additive noise. Such equations are more realistic than autonomous SPDEs…

Numerical Analysis · Mathematics 2020-11-18 Jean Daniel Mukam , Antoine Tambue