paper-with-me

홈 › Papers

Quantum Sub-Gaussian Mean Estimator

2021-08-27 · Yassine Hamoudi

We present a new quantum algorithm for estimating the mean of a real-valued random variable obtained as the output of a quantum computation. Our estimator achieves a nearly-optimal quadratic speedup over the number of classical i.i.d. samples needed to estimate the mean of a heavy-tailed distribution with a sub-Gaussian error rate. This result subsumes (up to logarithmic factors) earlier works on the mean estimation problem that were not optimal for heavy-tailed distributions [BHMT02,BDGT11], or that require prior information on the variance [Hein02,Mon15,HM19]. As an application, we obtain new quantum algorithms for the $(\epsilon,\delta)$-approximation problem with an optimal dependence on the coefficient of variation of the input random variable.

📄 PDF Abstract BibTeX arXiv:2108.12172

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Near-Optimal Quantum Algorithms for Multivariate Mean Estimation

2021-11-18 · Arjan Cornelissen, Yassine Hamoudi, Sofiene Jerbi

We propose the first near-optimal quantum algorithm for estimating in Euclidean norm the mean of a vector-valued random variable with finite mean and covariance. Our result aims at extending the theory of multivariate su…

Quantum Heavy-tailed Bandits

2023-01-23 · Yulian Wu, Chaowen Guan, Vaneet Aggarwal, Di Wang

In this paper, we study multi-armed bandits (MAB) and stochastic linear bandits (SLB) with heavy-tailed rewards and quantum reward oracle. Unlike the previous work on quantum bandits that assumes bounded/sub-Gaussian dis…

Multi-Armed Bandits

Quantum Speedups for Stochastic Optimization with Heavy-Tailed Noise

2026-07-28 · Bin Luo, Chengchang Liu, Jonathan Allcock, Shengyu Zhang 외 arxiv

We study stochastic optimization with heavy-tailed gradient noise. We first propose a novel quantum mean estimator for multivariate heavy-tailed random variables that achieves lower query complexity than optimal classica…

Stochastic Optimization

Quantum Clustering and Gaussian Mixtures

2016-12-29 · Mahajabin Rahman, Davi Geiger

The mixture of Gaussian distributions, a soft version of k-means , is considered a state-of-the-art clustering algorithm. It is widely used in computer vision for selecting classes, e.g., color, texture, and shapes. In t…

Clustering

Estimation of low rank density matrices by Pauli measurements

2016-10-16 · Dong Xia

Density matrices are positively semi-definite Hermitian matrices with unit trace that describe the states of quantum systems. Many quantum systems of physical interest can be represented as high-dimensional low rank dens…

Quantum State Tomography