Multi-feature Machine Learning with Quantum Superposition
Multi-feature Machine Learning with Quantum Superposition
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DOI：
10.1109/icce-asia57006.2022.9954778
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Tuyen Nguyen;Incheon Paik;Truong Cong Thang
中科院分区：
文献类型：
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作者：
Tuyen Nguyen;Incheon Paik;Truong Cong Thang
一般的量子计算和特别的量子机器学习为解决复杂问题提供了有前途的方法。量子叠加是量子计算中最重要的特性之一，它允许我们同时嵌入和处理不同的信息源（例如多模态或多视图输入）。在这项研究中，我们首次研究基于量子叠加的多特征机器学习。我们表明，当使用叠加的量子数据进行训练时，量子模型可以轻松扩展其学习空间，从而比单特征学习获得显着改进。
Quantum computing in general and quantum machine learning in particular provide promising approaches to solve complex problems. Quantum superposition, which is one of the most important characteristics in quantum computing, allows us to embed and process different information sources (e.g. multi-modality or multi-view inputs) simultaneously. In this study, we investigate for the first time multi-feature machine learning based on quantum superposition. We show that when training with superposed quantum data, the quantum models can easily extend their learning space to gain a significant improvement over the single-feature learning.