利用量子退火方法求解考卷组成优化的量子算法设计与实现

IF 4.9 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Chu-Fu Wang;Yih-Kai Lin;Ling Cheng
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引用次数: 0

摘要

在测试系统中,项目反应理论是一种广泛使用的准确综合用户反应信息的模型。然而,与经典的测试理论方法相比,它带来了更高的计算负担,增加了系统设计的复杂性。量子计算在缓解这些计算挑战方面显示出了希望。目前,通用量子计算机仍处于相对早期的发展阶段。然而,专用量子计算架构已经被设计用于解决组合优化问题,引起了各个领域的极大关注。这些系统使研究人员能够在减少计算时间的情况下解决特定领域的优化问题。据我们所知,在教育技术领域还没有提出量子计算的应用。因此,本研究旨在设计一种量子二次型无约束二元优化配方,用于优化测试片成分。所提出的模型可以在实际的量子Ising机器(或更大量子位使用量的数字量子Ising机器)上实现,以评估系统效率。仿真结果表明,该方法在计算效率方面优于遗传算法和粒子群优化算法等传统方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantum Algorithm Design and Its Implementation for Solving Test Sheet Composition Optimization Using a Quantum Annealing Approach
In testing systems, the item response theory is a widely used model for accurately synthesizing user response information. However, compared to classical test theory approaches, it imposes a higher computational burden and increases the system design complexity. Quantum computing has shown promise in alleviating these computational challenges. Currently, general-purpose quantum computers are still in a relatively early stage of development. However, special-purpose quantum computing architectures have been designed to solve combinatorial optimization problems, attracting significant attention across various fields. These systems enable researchers to tackle domain-specific optimization problems with reduced computational time. To the best of our knowledge, no applications of quantum computing have been proposed in the field of educational technology. This study, therefore, aimed to design a quantum quadratic unconstrained binary optimization formulation for optimizing test sheet composition. The proposed model can be implemented on practical quantum Ising machines (or digital quantum Ising machines for larger qubit usage) to evaluate system efficiency. Simulation results demonstrate that the proposed approach outperforms traditional methods, including the genetic algorithm and particle swarm optimization algorithm, in terms of computational efficiency.
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来源期刊
IEEE Transactions on Learning Technologies
IEEE Transactions on Learning Technologies COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
7.50
自引率
5.40%
发文量
82
审稿时长
>12 weeks
期刊介绍: The IEEE Transactions on Learning Technologies covers all advances in learning technologies and their applications, including but not limited to the following topics: innovative online learning systems; intelligent tutors; educational games; simulation systems for education and training; collaborative learning tools; learning with mobile devices; wearable devices and interfaces for learning; personalized and adaptive learning systems; tools for formative and summative assessment; tools for learning analytics and educational data mining; ontologies for learning systems; standards and web services that support learning; authoring tools for learning materials; computer support for peer tutoring; learning via computer-mediated inquiry, field, and lab work; social learning techniques; social networks and infrastructures for learning and knowledge sharing; and creation and management of learning objects.
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