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首页> 外文期刊>Journal of Quantum Information Science >Empirical Analysis of a Quantum Classifier Implemented on IBM’s 5Q Quantum Computer
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Empirical Analysis of a Quantum Classifier Implemented on IBM’s 5Q Quantum Computer

机译：在IBM 5Q Quantum计算机上实施的量子分类器的经验分析

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The development of artificial intelligence today is marked with increased computational power, new algorithms, and big data. One such milestone impressive achievement in this area is Google’s Al style="font-family:Verdana;">p style="font-family:Verdana;">haGo. However, this advancement is beginning to face increasing challenges and the major bottleneck of AI today is the lack of adequate computing power in the processing of big data. Quantum computing offers a new and viable solution to deal with these challenges. A recent work designed a quantum classifier that runs on IBM’s five qubit quantum computer and tested its performance on the Iris data set as well as style="font-family:Verdana;">a style="font-family:Verdana;"> circles data set. As quantum machine learning is still an emerging discipline, it may be enlightening to conduct an empirical analysis of this quantum classifier on some artificial datasets to help learn its unique features and potentials. Our work on the quantum classifier can be summarized in three parts. The first is to run its original version as a binary classifier on some artificial datasets using visualization to reveal the quantum nature of this algorithm, and the second is to analyze the swap operation utilized in its original circuit due to the hardware constraint and investigate its impact on the performance of the classifier. The last part is to extend the original circuit for binary classification to a circuit for multiclass classification and test its performance. Our findings shed new light on how this quantum classifier works.
机译：如今，人工智能的发展以计算能力增强，新算法和大数据为标志。 Google的Al style =“ font-family：Verdana;”> p style =“ font-family：Verdana;”> haGo就是这一领域具有里程碑意义的成就。但是，这种进步开始面临越来越多的挑战，而当今AI的主要瓶颈是在处理大数据时缺乏足够的计算能力。量子计算提供了一种新的可行的解决方案来应对这些挑战。最近的一项工作设计了一个量子分类器，该分类器可在IBM的5量子位量子计算机上运行，​​并在Iris数据集以及 style =“ font-family：Verdana;”> a < span style =“ font-family：Verdana;”>圈出数据集。由于量子机器学习仍是一门新兴学科，因此在一些人工数据集上对该量子分类器进行实证分析以帮助学习其独特功能和潜力可能会有所启发。我们在量子分类器上的工作可以分为三个部分。首先是使用可视化技术在某些人工数据集上将其原始版本作为二进制分类器运行，以揭示该算法的量子性质，其次是分析由于硬件限制而在其原始电路中使用的交换操作并研究其影响关于分类器的性能。最后一部分是将原始的用于二进制分类的电路扩展到用于多分类的电路并测试其性能。我们的发现为这种量子分类器的工作原理提供了新的思路。

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