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arXiv 2410.09121quant-phcs.ETcs.ITcs.LGmath.IT
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量子编码技术比较

Comparing Quantum Encoding Techniques

Nidhi Munikote

AI总结:

本研究面向混合量子-经典机器学习场景,采用QuClassi架构对MNIST数据集的3、6数字做二分类,从性能、抗噪性、资源消耗等维度对比了基、振幅、旋转三类基础量子编码方法。

摘要:

随着量子计算机的计算能力持续提升,其应用场景也不断拓展。例如,量子技术正与经典神经网络结合以开展机器学习任务。要实现这类应用,或是量子化学模拟、密码学应用等其他广泛用途,必须通过量子编码将经典数据转换为量子态。目前有三种基础编码方法:基编码、振幅编码与旋转编码,此外还有若干已提出的组合编码方案。本研究针对混合量子-经典机器学习场景,对这些编码方法展开探究。研究采用QuClassi量子神经网络架构,对MNIST数据集中的数字“3”和“6”进行二分类,在考量资源占用与计算复杂度的同时,获取了准确率、熵、损失以及抗噪性等多项指标,以此对三种主流编码方法进行比较。

英文摘要:

As quantum computers continue to become more capable, the possibilities of their applications increase. For example, quantum techniques are being integrated with classical neural networks to perform machine learning. In order to be used in this way, or for any other widespread use like quantum chemistry simulations or cryptographic applications, classical data must be converted into quantum states through quantum encoding. There are three fundamental encoding methods: basis, amplitude, and rotation, as well as several proposed combinations. This study explores the encoding methods, specifically in the context of hybrid quantum-classical machine learning. Using the QuClassi quantum neural network architecture to perform binary classification of the `3' and `6' digits from the MNIST datasets, this study obtains several metrics such as accuracy, entropy, loss, and resistance to noise, while considering resource usage and computational complexity to compare the three main encoding methods.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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