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Group convolutions and cross-correlations, which are equivariant to the actions of group elements, are commonly used in mathematics to analyze or take advantage of symmetries inherent in a given problem setting. Here, we provide efficient…

We propose a learning-based approach for the sparse Gaussian Elimination. There are many hard combinatorial optimization problems in modern sparse solver. These NP-hard problems could be handled in the framework of Markov Decision Process,…

Numerical Analysis · Mathematics 2021-10-01 Yingshi Chen

In this work, we generalize the integer enumeration basis. We also construct bijections between the elements of special sets and the elements of some groups, and treat the special case of the hyperoctohedral groups. Then, we find a code…

Number Theory · Mathematics 2014-11-14 F. Patrick Rabarison , Hery Randriamaro

In this study, we demonstrate the possibility of the implementation of universal Gaussian computation on a two-node cluster state ensemble. We consider the phase-locked sub-Poissonian lasers, which radiate the bright light with squeezed…

Quantum Physics · Physics 2019-06-26 S. B. Korolev , A. N. Dobrotvorskaia , T. Yu. Golubeva , Yu. M. Golubev

The required set of operations for universal continuous-variable quantum computation can be divided into two primary categories: Gaussian and non-Gaussian operations. Furthermore, any Gaussian operation can be decomposed as a sequence of…

Quantum Physics · Physics 2020-02-12 Kunal Sharma , Mark M. Wilde

Scalable Gaussian process (GP) inference is essential for sequential decision-making tasks, yet improving GP scalability remains a challenging problem with many open avenues of research. This paper focuses on iterative GPs, where iterative…

Machine Learning · Computer Science 2025-11-21 Alan Yufei Dong , Jihao Andreas Lin , José Miguel Hernández-Lobato

Gaussian processes retain the linear model either as a special case, or in the limit. We show how this relationship can be exploited when the data are at least partially linear. However from the perspective of the Bayesian posterior, the…

Methodology · Statistics 2008-07-13 Robert B. Gramacy , Herbert K. H. Lee

We propose a general modeling and algorithmic framework for discrete structure recovery that can be applied to a wide range of problems. Under this framework, we are able to study the recovery of clustering labels, ranks of players, signs…

Statistics Theory · Mathematics 2020-09-29 Chao Gao , Anderson Y. Zhang

In the last five years, the financial industry has been impacted by the emergence of digitalization and machine learning. In this article, we explore two methods that have undergone rapid development in recent years: Gaussian processes and…

Portfolio Management · Quantitative Finance 2019-03-13 Joan Gonzalvez , Edmond Lezmi , Thierry Roncalli , Jiali Xu

We present a quantum algorithm solving the greatest common divisor (GCD) problem. This quantum algorithm possesses similar computational complexity with classical algorithms, such as the well-known Euclidean algorithm for GCD. This…

Quantum Physics · Physics 2017-08-02 Wen Wang , Xu Jiang , Liang-zhu Mu , Heng Fan

The aim of this paper is to develop novel quantum algorithms for Gaussian process quadrature methods. Gaussian process quadratures are numerical integration methods where Gaussian processes are used as functional priors for the integrands…

Computation · Statistics 2025-02-21 Cristian A. Galvis-Florez , Ahmad Farooq , Simo Särkkä

We introduce a novel relaxation of combinatorial discrepancy called Gaussian discrepancy, whereby binary signings are replaced with correlated standard Gaussian random variables. This relaxation effectively reformulates an optimization…

Discrete Mathematics · Computer Science 2022-08-11 Sinho Chewi , Patrik Gerber , Philippe Rigollet , Paxton Turner

A numerical algorithm that computes the decomposition of any finite-dimen\-sio\-nal unitary reducible representation of a compact Lie group is presented. The algorithm, which does not rely on an algebraic insight on the group structure, is…

Mathematical Physics · Physics 2024-01-19 Alberto Ibort , Alberto López-Yela , Julio Moro

A major limitation of exact inference algorithms for probabilistic graphical models is their extensive memory usage, which often puts real-world problems out of their reach. In this paper we show how we can extend inference algorithms,…

Artificial Intelligence · Computer Science 2012-03-19 Kalev Kask , Rina Dechter , Andrew E. Gelfand

Finite Gaussian mixture models provide a powerful and widely employed probabilistic approach for clustering multivariate continuous data. However, the practical usefulness of these models is jeopardized in high-dimensional spaces, where…

Methodology · Statistics 2022-05-13 Alessandro Casa , Andrea Cappozzo , Michael Fop

We present a quantum algorithm which identifies with certainty a hidden subgroup of an arbitrary finite group G in only a polynomial (in log |G|) number of calls to the oracle. This is exponentially better than the best classical algorithm.…

Quantum Physics · Physics 2016-12-30 Mark Ettinger , Peter Hoyer , Emanuel Knill

We survey group-theoretic algorithms for finding (some or all) subgroups of a finite group and discuss the implementation of these algorithms in the computer algebra system GAP

Group Theory · Mathematics 2020-12-04 Alexander Hulpke

We develop a combinatorial approach to the study of semigroups and monoids with finite presentations satisfying small overlap conditions. In contrast to existing geometric methods, our approach facilitates a sequential left-right analysis…

Rings and Algebras · Mathematics 2007-12-04 Mark Kambites

In this paper we give a polynomial-time quantum algorithm for computing orders of solvable groups. Several other problems, such as testing membership in solvable groups, testing equality of subgroups in a given solvable group, and testing…

Quantum Physics · Physics 2007-05-23 John Watrous

The state-of-the-art linked Gaussian process offers a way to build analytical emulators for systems of computer models. We generalize the closed form expressions for the linked Gaussian process under the squared exponential kernel to a…

Methodology · Statistics 2021-02-09 Deyu Ming , Serge Guillas