量子和经典有序二元决策图的指数分离,重排序方法和层次

IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Natural Computing Pub Date : 2022-07-26 DOI:10.1007/s11047-022-09904-3
Kamil Khadiev, Aliya Khadieva, Alexander Knop
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引用次数: 4

摘要

本文研究了量子有序二元决策图(\(\mathrm {OBDD}\))模型;就“宽度”复杂度而言,它是只读一次量子分支程序的限制版本。已知确定性复杂度和量子复杂度之间的最大差距是指数级的。但是很少有函数有这样的差距。我们提出了一种新的技术(“重新排序”)来证明具有任意阶输入变量的OBDD的下界和上界,如果我们有相似的自然阶的边界。利用这种变换,我们构造了一个总函数\(\mathrm {REQ}\),使它的确定性\(\mathrm {OBDD}\)复杂度至少为\(2^{\varOmega (n / \log n)}\),量子\(\mathrm {OBDD}\)复杂度最多为\(O(n^2/\log n)\)。对于不能用线性宽度的\(\mathrm {OBDD}\) s表示的显式函数,这是已知的最大间隙。另一个函数(移位相等函数)允许我们获得\(2^{\varOmega (n)}\) vs \(O(n^2)\)的差距。此外,我们还证明了布尔函数复杂度类的有界误差量子和概率\(\mathrm {OBDD}\)宽度层次结构。此外,使用“重新排序”方法,我们扩展了一个层次结构,用于读取k次有序二进制决策图(\({\textit{k}}\text {-}\mathrm {OBDD}\))的多项式宽度,为\(k = o(n / \log ^3 n)\)。我们证明了多项式、上多项式和次指数宽度的有界误差概率\({\textit{k}}\text {-}\mathrm {OBDD}\) s的类似层次结构。这项工作的扩展摘要在俄罗斯国际计算机科学研讨会上发表,CSR 2017,喀山,俄罗斯,2017年6月8日至12日
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Exponential separation between quantum and classical ordered binary decision diagrams, reordering method and hierarchies

In this paper, we study quantum Ordered Binary Decision Diagrams(\(\mathrm {OBDD}\)) model; it is a restricted version of read-once quantum branching programs, with respect to “width” complexity. It is known that the maximal gap between deterministic and quantum complexities is exponential. But there are few examples of functions with such a gap. We present a new technique (“reordering”) for proving lower bounds and upper bounds for OBDD with an arbitrary order of input variables if we have similar bounds for the natural order. Using this transformation, we construct a total function \(\mathrm {REQ}\) such that the deterministic \(\mathrm {OBDD}\) complexity of it is at least \(2^{\varOmega (n / \log n)}\), and the quantum \(\mathrm {OBDD}\) complexity of it is at most \(O(n^2/\log n)\). It is the biggest known gap for explicit functions not representable by \(\mathrm {OBDD}\)s of a linear width. Another function(shifted equality function) allows us to obtain a gap \(2^{\varOmega (n)}\) vs \(O(n^2)\). Moreover, we prove the bounded error quantum and probabilistic \(\mathrm {OBDD}\) width hierarchies for complexity classes of Boolean functions. Additionally, using “reordering” method we extend a hierarchy for read-k-times Ordered Binary Decision Diagrams (\({\textit{k}}\text {-}\mathrm {OBDD}\)) of polynomial width, for \(k = o(n / \log ^3 n)\). We prove a similar hierarchy for bounded error probabilistic \({\textit{k}}\text {-}\mathrm {OBDD}\)s of polynomial, superpolynomial and subexponential width. The extended abstract of this work was presented on International Computer Science Symposium in Russia, CSR 2017, Kazan, Russia, June 8 – 12, 2017 Khadiev and Khadieva (2017)

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来源期刊
Natural Computing
Natural Computing Computer Science-Computer Science Applications
CiteScore
4.40
自引率
4.80%
发文量
49
审稿时长
3 months
期刊介绍: The journal is soliciting papers on all aspects of natural computing. Because of the interdisciplinary character of the journal a special effort will be made to solicit survey, review, and tutorial papers which would make research trends in a given subarea more accessible to the broad audience of the journal.
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