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Pseudorandom number generators (PRNGs) are ubiquitous in stochastic simulations and machine learning (ML), where they drive sampling, parameter initialization, regularization, and data shuffling. While widely used, the potential impact of…

Performance · Computer Science 2025-10-30 Benjamin A. Antunes

We develop techniques to prove lower bounds for the BCAST(log n) Broadcast Congested Clique model (a distributed message passing model where in each round, each processor can broadcast an O(log n)-sized message to all other processors). Our…

Distributed, Parallel, and Cluster Computing · Computer Science 2019-05-21 Lijie Chen , Ofer Grossman

Pseudorandom codes (PRCs), introduced by Christ and Gunn (CRYPTO '2024), are error-correcting codes whose codewords are computationally indistinguishable from uniformly random strings, while still being decodable by someone holding the key.…

Cryptography and Security · Computer Science 2026-05-20 Shengtang Huang , Xin Li , Songtao Mao , Zhaienhe Zhou

Probabilistic circuits (PCs) are a tractable representation of probability distributions allowing for exact and efficient computation of likelihoods and marginals. There has been significant recent progress on improving the scale and…

Machine Learning · Computer Science 2022-11-24 Meihua Dang , Anji Liu , Guy Van den Broeck

We study the problem of obtaining deterministic black-box polynomial identity testing algorithms (PIT) for algebraic branching programs (ABPs) that are read-once and oblivious. This class has an deterministic white-box polynomial identity…

Computational Complexity · Computer Science 2013-09-24 Michael A. Forbes , Amir Shpilka

Let X be randomly chosen from {-1,1}^n, and let Y be randomly chosen from the standard spherical Gaussian on R^n. For any (possibly unbounded) polytope P formed by the intersection of k halfspaces, we prove that |Pr [X belongs to P] - Pr [Y…

Computational Complexity · Computer Science 2013-02-05 Prahladh Harsha , Adam Klivans , Raghu Meka

NIST SP800-22 (2010) proposes the state of art testing suite for (pseudo) random generators to detect deviations of a binary sequence from randomness. On the one hand, as a counter example to NIST SP800-22 test suite, it is easy to…

Cryptography and Security · Computer Science 2014-01-15 Yongge Wang

Universality, namely distributional invariance, is a well-known property for many random structures. For example, it is known to hold for a broad range of variational problems with random input. Much less is known about the algorithmic…

Data Structures and Algorithms · Computer Science 2025-12-25 Houssam El Cheairi , David Gamarnik

Pseudorandom circuits generate quantum states and unitary operators which are approximately distributed according to the unitarily invariant Haar measure. We explore how several design parameters affect the efficiency of pseudo-random…

Quantum Physics · Physics 2009-11-13 Yaakov S. Weinstein , Winton G. Brown , Lorenza Viola

The advantages of quantum random number generators (QRNGs) over pseudo-random number generators (PRNGs) are normally attributed to the nature of quantum measurements. This is often seen as implying the superiority of the sequences of bits…

Quantum Physics · Physics 2019-02-04 Alastair A. Abbott , Cristian S. Calude , Michael J. Dinneen , Nan Huang

Random constraint satisfaction problems (CSPs) such as random $3$-SAT are conjectured to be computationally intractable. The average case hardness of random $3$-SAT and other CSPs has broad and far-reaching implications on problems in…

Computational Complexity · Computer Science 2019-11-11 Jonah Brown-Cohen , Prasad Raghavendra

A curious property of randomized log-space search algorithms is that their outputs are often longer than their workspace. This leads to the question: how can we reproduce the results of a randomized log space computation without storing the…

Computational Complexity · Computer Science 2018-03-14 Ofer Grossman , Yang P. Liu

An M-sequence generated by a primitive polynomial has many interesting and desirable properties. A pseudo-random array is the two-dimensional generalization of an M-sequence. There are non-primitive polynomials all of whose non-zero…

Information Theory · Computer Science 2025-08-20 Simon Blackburn , Yeow Meng Chee , Tuvi Etzion , Huimin Lao

Machine learning (ML) frameworks rely heavily on pseudorandom number generators (PRNGs) for tasks such as data shuffling, weight initialization, dropout, and optimization. Yet, the statistical quality and reproducibility of these…

Other Computer Science · Computer Science 2025-07-08 Benjamin A. Antunes

We show that a black-box construction of a pseudorandom generator from a one-way function needs to make Omega(n/log(n)) calls to the underlying one-way function. The bound even holds if the one-way function is guaranteed to be regular. In…

Cryptography and Security · Computer Science 2012-05-22 Thomas Holenstein , Makrand Sinha

Pseudo-random number generators are widely used in many branches of science, mainly in applications related to Monte Carlo methods, although they are deterministic in design and, therefore, unsuitable for tackling fundamental problems in…

Fast classical processing is essential for most quantum fault-tolerance architectures. We introduce a sliding-window decoding scheme that provides fast classical processing for the surface code through parallelism. Our scheme divides the…

Quantum Physics · Physics 2022-10-03 Xinyu Tan , Fang Zhang , Rui Chao , Yaoyun Shi , Jianxin Chen

The quest for a cryptographically secure pseudorandom bit generator (PRBG) was initiated long ago, and for a long time the proposed pseudorandom generators were very slow. More recently some "provably secure" PRBG capable to achieve a…

Cryptography and Security · Computer Science 2013-12-24 Corbo Antonio Esposito , Didone Fabiola

We study the arithmetic complexity of hitting set generators, which are pseudorandom objects used for derandomization of the polynomial identity testing problem. We give new explicit constructions of hitting set generators whose outputs are…

Computational Complexity · Computer Science 2025-08-19 Robert Andrews

In this paper we explore the decision regions of Linear Programming (LP) decoding. We compare the decision regions of an LP decoder, a Belief Propagation (BP) decoder and the optimal Maximum Likelihood (ML) decoder. We study the effect of…

Information Theory · Computer Science 2011-06-15 Asi Lifshitz , Yair Be'ery