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We present a quantum algorithm for estimating the matrix determinant based on quantum spectral sampling. The algorithm estimates the logarithm of the determinant of an $n \times n$ positive sparse matrix to an accuracy $\epsilon$ in time…

Quantum Physics · Physics 2025-05-02 Vittorio Giovannetti , Seth Lloyd , Lorenzo Maccone

We investigate the approximation for computing the sum $a_1+...+a_n$ with an input of a list of nonnegative elements $a_1,..., a_n$. If all elements are in the range $[0,1]$, there is a randomized algorithm that can compute an…

Data Structures and Algorithms · Computer Science 2012-03-01 Bin Fu , Wenfeng Li , Zhiyong Peng

Estimating cardinality, i.e., the number of distinct elements, of a data stream is a fundamental problem in areas like databases, computer networks, and information retrieval. This study delves into a broader scenario where each element…

Databases · Computer Science 2024-06-28 Yiyan Qi , Rundong Li , Pinghui Wang , Yufang Sun , Rui Xing

Consider the problem of estimating the median of N items to a precision epsilon, i.e., the estimate should be such that, with a high probability, the number of items, with values both smaller than and larger than this estimate, is less than…

Quantum Physics · Physics 2007-05-23 Lov K. Grover

This work presents new cardinality estimation methods for data sets recorded by HyperLogLog sketches. A simple derivation of the original estimator was found, that also gives insight how to correct its deficiencies. The result is an…

Data Structures and Algorithms · Computer Science 2017-06-23 Otmar Ertl

We exhibit a probabilistic algorithm which computes a rational point of an absolutely irreducible variety over a finite field defined by a reduced regular sequence. Its time--space complexity is roughly quadratic in the logarithm of the…

Number Theory · Mathematics 2007-05-23 Antonio Cafure , Guillermo Matera

Identifying independence between two random variables or correlated given their samples has been a fundamental problem in Statistics. However, how to do so in a space-efficient way if the number of states is large is not quite well-studied.…

Data Structures and Algorithms · Computer Science 2022-11-21 Zhenhao Gu , Hao Zhang

In the problem of high-dimensional convexity testing, there is an unknown set $S \subseteq \mathbb{R}^n$ which is promised to be either convex or $\varepsilon$-far from every convex body with respect to the standard multivariate normal…

Computational Complexity · Computer Science 2017-06-29 Xi Chen , Adam Freilich , Rocco A. Servedio , Timothy Sun

We consider ordinal online problems, i.e., tasks that only require pairwise comparisons between elements of the input. A classic example is the secretary problem and the game of googol, as well as its multiple combinatorial extensions such…

Data Structures and Algorithms · Computer Science 2023-10-13 Nick Gravin , Enze Sun , Zhihao Gavin Tang

As we move towards future galaxy surveys, the three-point statistics will be increasingly leveraged to enhance the constraining power of the data on cosmological parameters. An essential part of the three-point function estimation is…

Cosmology and Nongalactic Astrophysics · Physics 2019-05-22 David W. Pearson , Lado Samushia

We give an algorithm which produces a unique element of the Clifford group $\mathcal{C}_n$ on $n$ qubits from an integer $0\le i < |\mathcal{C}_n|$ (the number of elements in the group). The algorithm involves $O(n^3)$ operations. It is a…

Quantum Physics · Physics 2022-12-11 Robert Koenig , John A. Smolin

Predicting the winner of an election is a favorite problem both for news media pundits and computational social choice theorists. Since it is often infeasible to elicit the preferences of all the voters in a typical prediction scenario, a…

Data Structures and Algorithms · Computer Science 2016-04-21 Arnab Bhattacharyya , Palash Dey

We investigate parameterizing hard combinatorial problems by the size of the solution set compared to all solution candidates. Our main result is a uniform sampling algorithm for satisfying assignments of 2-CNF formulas that runs in…

Discrete Mathematics · Computer Science 2017-08-04 Jean Cardinal , Jerri Nummenpalo , Emo Welzl

We describe an algorithm for using a quantum computer to calculate mean values of observables and the partition function of a quantum system. Our algorithm includes two sub-algorithms. The first sub-algorithm is for calculating, with…

Quantum Physics · Physics 2010-11-10 Robert R. Tucci

Sampling edges from a graph in sublinear time is a fundamental problem and a powerful subroutine for designing sublinear-time algorithms. Suppose we have access to the vertices of the graph and know a constant-factor approximation to the…

Data Structures and Algorithms · Computer Science 2022-11-15 Talya Eden , Shyam Narayanan , Jakub Tětek

In this paper we propose new techniques to sample arbitrary third-order tensors, with an objective of speeding up tensor algorithms that have recently gained popularity in machine learning. Our main contribution is a new way to select, in a…

Machine Learning · Statistics 2015-02-23 Srinadh Bhojanapalli , Sujay Sanghavi

We develop a new algorithmic technique that allows to transfer some constant time approximation algorithms for general graphs into random order streaming algorithms. We illustrate our technique by proving that in random order streams with…

Data Structures and Algorithms · Computer Science 2017-11-15 Pan Peng , Christian Sohler

We study the set of numbers the total number of independent sets can admit in $n$-vertex graphs. In this paper, we prove that the cardinality $\mathcal{N}i(n)$ of this set is very close to $2^n$ in the following sense: $\mathcal{N}i(n)/2^n…

Combinatorics · Mathematics 2025-10-07 Benedek Kovács , Zoltán Lóránt Nagy

We consider n by n real matrices whose entries are non-degenerate random variables that are independent but non necessarily identically distributed, and show that the probability that such a matrix is singular is O(1/sqrt{n}). The purpose…

Probability · Mathematics 2008-01-09 Laurent Bruneau , Francois Germinet

Simple Monte Carlo is a versatile computational method with a convergence rate of $O(n^{-1/2})$. It can be used to estimate the means of random variables whose distributions are unknown. Bernoulli random variables, $Y$, are widely used to…

Numerical Analysis · Mathematics 2014-11-06 Lan Jiang , Fred J. Hickernell