arXiv subjects

Marek Kwas

Publications and source records attributed to Marek Kwas.

3 recordsLinked to original sources

arXiv2004

Complexity of multivariate Feynman-Kac path integration in randomized and quantum settings

The Feynman-Kac path integration problem was studied in the worst case setting by Plaskota et al. (J. Comp. Phys. 164 (2000) 335) for the univariate case and by Kwas and Li (J. Comp. 19 (2003) 730) for the multivariate case with d space variables. In this paper we consider the multivariate Feynman-Kac path integration problem in the randomized and quantum settings. For smooth multivariate functions, it was proven in Kwas and Li (2003) that the classical worst case complexity suffers from the curse of dimensionality in d. We show that in both the randomized and quantum settings the curse of dimensionality is vanquished, i.e., the number of function evaluations and/or quantum queries required to compute an e-approximation has a bound independent of d and depending polynomially on 1/e. The exponents of these polynomials are at most 2 in the randomized setting and at most 1 in the quantum setting. Hence we have exponential speedup over the classical worst case setting and quadratic speedup of the quantum setting over the randomized setting. However, both the randomized and quantum algorithms presented here still require extensive precomputing, similar to the algorithms of Plaskota et al. (2000) and Kwas and Li(2003).

quant-ph↗

arXiv2003

Quantum Boolean Summation with Repetitions in the Worst-Average Setting

We study the quantum summation QS algorithm of Brassard, Hoyer, Mosca and Tapp, which approximates the arithmetic mean of a Boolean function defined on $N$ elements. We present sharp error bounds of the QS algorithm in the worst-average setting with the average performance measured in the $L_q$ norm, $q \in [1,\infty]$. We prove that the QS algorithm with $M$ quantum queries, $M<N$, has the worst-average error bounds of the form $Θ(\ln M/M)$ for $q=1$, $Θ(M^{-1/q})$ for $q\in (1,\infty)$, and is equal to 1 for $q=\infty$. We also discuss the asymptotic constants of these estimates. We improve the error bounds by using the QS algorithm with repetitions. Using the number of repetitions which is independent of $M$ and linearly dependent on $q$, we get the error bound of order $M^{-1}$ for any $q \in [1,\infty)$. Since $Ω(M^{-1})$ is a lower bound on the worst-average error of any quantum algorithm with $M$ queries, the QS algorithm with repetitions is optimal in the worst-average setting.

quant-ph↗

arXiv2003

Sharp Error Bounds on Quantum Boolean Summation in Various Settings

We study the quantum summation (QS) algorithm of Brassard, Hoyer, Mosca and Tapp, that approximates the arithmetic mean of a Boolean function defined on N elements. We improve error bounds presented in [1] in the worst-probabilistic setting, and present new error bounds in the average-probabilistic setting. In particular, in the worst-probabilistic setting, we prove that the error of the QS algorithm using $M - 1$ queries is $3π/(4M)$ with probability $8/π^2$, which improves the error bound $πM^{-1} + π^2 M^{-2}$ of Brassard et al. We also present bounds with probabilities $p\in (1/2, 8/π^2]$ and show they are sharp for large $M$ and $NM^{-1}$. In the average-probabilistic setting, we prove that the QS algorithm has error of order $\min\{M^{-1}, N^{-1/2}\}$ if $M$ is divisible by 4. This bound is optimal, as recently shown in [10]. For M not divisible by 4, the QS algorithm is far from being optimal if $M \ll N^{1/2}$ since its error is proportional to $M^{-1}^$.

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