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Метод моделирования квантовых вычислений на основе QuIDD-графов

Метод моделирования квантовых вычислений на основе QuIDD-графов

Abstract

Для моделирования квантовых вычислений характерно использование матриц большого размера, содержащих малое количество различных элементов. Наиболее эффективный способ снижения вычислительных затрат при моделировании квантовых вычислений был предложен университетом Мичигана и получил название QuIDD. В данной статье приводятся его ключевые аспекты и предлагаются уровнево-рекурсивные методы редукции QuIDD -графов и поэлементных операций над ними, способные повысить эффективность базовой методики. Изложенные в статье методы могут быть использованы при построении графовой математической модели универсального квантового вычислителя, позволяющей моделировать вычисления на квантовом регистре с предельной размерностью порядка 50 q-бит.

Quantum computer simulation requires processing of huge-size matrixes containing several distinct elements. The most effective method of decreasing calculation costs for simulation was developed in the University of Michigan and was called Quantum Information Decision Diagrams. In order to improve the efficiency of the base QuIDD methodic this article covers its key aspects and advises level-wise recursive methods of QuIDD graph reduction and matrix member-wise operations. Methods set out in this article can be used for developing a graph-based mathematical model of a universal quantum computer, operating at the maximal dimension of the quantum register of about 50 q-bits.

Keywords

КВАНТОВЫЕ ВЫЧИСЛЕНИЯ, МОДЕЛИРОВАНИЕ, Q-БИТ, QUIDD-ГРАФ, МАТРИЦЫ, ВЕКТОР СОСТОЯНИЯ, ТЕНЗОРНОЕ ПРОИЗВЕДЕНИЕ, ПОУРОВНЕВЫЕ ОПЕРАЦИИ

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average