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A bound on the error introduced by truncating a quantum addition is given. This bound shows that only a few controlled rotation gates will be necessary to get a reliable computation.

Quantum Physics · Physics 2007-05-23 Nathan W. Panike

Let $q \in (0,1)$ and $\delta \in (0,1)$ be real numbers, and let $C$ be a coin that comes up heads with an unknown probability $p$, such that $p \neq q$. We present an algorithm that, on input $C$, $q$, and $\delta$, decides, with…

Data Structures and Algorithms · Computer Science 2020-11-12 Luís Fernando Schultz Xavier da Silveira , Michiel Smid

In quantitative finance, modeling the volatility structure of underlying assets is vital to pricing options. Rough stochastic volatility models, such as the rough Bergomi model [Bayer, Friz, Gatheral, Quantitative Finance 16(6), 887-904,…

Computational Finance · Quantitative Finance 2021-12-16 Christian Bayer , Eric Joseph Hall , Raúl Tempone

In this paper, we study Bernoulli random sequences, i.e., sequences that are Martin-L\"of random with respect to a Bernoulli measure $\mu_p$ for some $p\in[0,1]$, where we allow for the possibility that $p$ is noncomputable. We focus in…

Logic · Mathematics 2019-03-26 Christopher P. Porter

In parallelized Monte-Carlo simulations, the order of summation is not always the same. When the mean is calculated in running fashion, this may create an artificial randomness in results which ought to be reproducible. This note takes a…

Computational Finance · Quantitative Finance 2022-09-13 Jherek Healy

Simulations of motion by mean curvature in bounded domains, with applications to bubble motion and grain growth, rely upon boundary conditions that are only approximately compatible with the equation of motion. Three closed form solutions…

Soft Condensed Matter · Physics 2019-01-03 Simon Cox , Gennady Mishuris

Randomization has been applied to Hamiltonian simulation in a number of ways to improve the accuracy or efficiency of product formulas. Deterministic product formulas are often constructed in a symmetric way to provide accuracy of even…

Quantum Physics · Physics 2024-08-12 Chien Hung Cho , Dominic W. Berry , Min-Hsiu Hsieh

We describe an embarrassingly parallel, anytime Monte Carlo method for likelihood-free models. The algorithm starts with the view that the stochasticity of the pseudo-samples generated by the simulator can be controlled externally by a…

Machine Learning · Computer Science 2015-12-03 Edward Meeds , Max Welling

We introduce a series of numbers which serve as a generalization of Bernoulli, Euler numbers and binomial coefficients. Their properties are applied to solve a probability problem and suggest a statistical test for independence and…

Combinatorics · Mathematics 2013-05-09 Andrey Sarantsev

In a probabilistic reformulation of a combinatorial problem, we often face an optimization over a hypercube, which corresponds to the Bernoulli probability parameter for each binary variable in the primal problem. The combinatorial nature…

Machine Learning · Computer Science 2026-02-17 Thanawat Sornwanee

We illustrate the power of Experimental Mathematics and Symbolic Computation to suggest irrationality proofs of natural constants, and the determination of their irrationality measures. Sometimes such proofs can be fully automated, but…

Number Theory · Mathematics 2021-05-10 Doron Zeilberger , Wadim Zudilin

It is shown that the Truncated Euler Equations, i.e. a finite set of ordinary differential equations for the amplitude of the large-scale modes, can correctly describe the complex transitional dynamics that occur within the turbulent regime…

Chaotic Dynamics · Physics 2016-12-07 Vishwanath Shukla , Stephan Fauve , Marc Brachet

The multi-armed bandit (MAB) problem is a classical learning task that exemplifies the exploration-exploitation tradeoff. However, standard formulations do not take into account {\em risk}. In online decision making systems, risk is a…

Machine Learning · Computer Science 2020-08-04 Qiuyu Zhu , Vincent Y. F. Tan

We develop our previous works concerning the identification of the collection of significant factors determining some, in general, non-binary random response variable. Such identification is important, e.g., in biological and medical…

Statistics Theory · Mathematics 2014-06-05 Alexander V. Bulinski , Alexander S. Rakitko

We present two new methods for multivariate exponential analysis. In [7], we developed a new algorithm for reconstruction of univariate exponential sums by exploiting the rational structure of their Fourier coefficients and reconstructing…

Numerical Analysis · Mathematics 2025-04-29 Nadiia Derevianko , Lennart Aljoscha Hübner

A sliding window algorithm receives a stream of symbols and has to output at each time instant a certain value which only depends on the last $n$ symbols. If the algorithm is randomized, then at each time instant it produces an incorrect…

Formal Languages and Automata Theory · Computer Science 2018-02-22 Moses Ganardi , Danny Hucke , Markus Lohrey

The aim of this study is to devise numerical methods for dealing with very high-dimensional Bermudan-style derivatives. For such problems, we quickly see that we can at best hope for price bounds, and we can only use a simulation approach.…

Computational Finance · Quantitative Finance 2016-01-06 L. C. G. Rogers

We present an efficient algorithm for twirling a multi-qudit quantum state. The algorithm can be used for approximating the twirling operation in an ensemble of physical systems in which the systems cannot be individually accessed. It can…

Quantum Physics · Physics 2007-05-23 Geza Toth , Juan Jose Garcia-Ripoll

Truncated sum rules have been used to calculate the fundamental limits of the nonlinear susceptibilities; and, the results have been consistent with all measured molecules. However, given that finite-state models result in inconsistencies…

Optics · Physics 2016-09-08 Mark G. Kuzyk

Neural networks with binary weights are computation-efficient and hardware-friendly, but their training is challenging because it involves a discrete optimization problem. Surprisingly, ignoring the discrete nature of the problem and using…

Machine Learning · Computer Science 2020-08-19 Xiangming Meng , Roman Bachmann , Mohammad Emtiyaz Khan
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