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- W4327773897 abstract "Quantum machine learning (QML) is an emerging research area that combines quantum computation with classical machine learning (ML). The primary objective of QML is to enhance the performance of traditional ML algorithms by harnessing quantum phenomena. Inspired by the success of classical neural networks (NNs), their quantum analog, commonly known as quantum neural networks (QNNs), are widely being investigated. Despite the significant interest, the literature still lacks some concrete evidence about QNN's superiority over their classical counterparts, especially in practical applications. This paper empirically demonstrates a greater capacity in hybrid quantum neural networks (HQNNs) for a practical application, namely multi-class classification. In particular, we train both the HQNNs and their equivalent classical counterparts on the same data. We then benchmark the models' accuracy for quantifying model's capacity, where greater accuracy typically implies greater capacity. The results demonstrate a clear quantum advantage, i.e., greater capacity of HQNNs over their classical counterparts, where the HQNN models constantly achieve better accuracy. This superiority in performance by HQNNs serves as a foundational study for further investigation to magnify the quantum advantage in real-world applications of HQNNs." @default.
- W4327773897 created "2023-03-19" @default.
- W4327773897 creator A5002005849 @default.
- W4327773897 creator A5042977442 @default.
- W4327773897 date "2022-12-01" @default.
- W4327773897 modified "2023-10-02" @default.
- W4327773897 title "Demonstrating Quantum Advantage in Hybrid Quantum Neural Networks for Model Capacity" @default.
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- W4327773897 doi "https://doi.org/10.1109/icrc57508.2022.00011" @default.
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