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Quantum Machine Learning

Hybrid Quantum Neural Network in High-dimensional Data Classification

arXiv
Authors: Hao-Yuan Chen, Yen-Jui Chang, Shih-Wei Liao, Ching-Ray Chang

Year

2023

Paper ID

6335

Status

Preprint

Abstract Read

~2 min

Abstract Words

102

Citations

N/A

Abstract

The research explores the potential of quantum deep learning models to address challenging machine learning problems that classical deep learning models find difficult to tackle. We introduce a novel model architecture that combines classical convolutional layers with a quantum neural network, aiming to surpass state-of-the-art accuracy while maintaining a compact model size. The experiment is to classify high-dimensional audio data from the Bird-CLEF 2021 dataset. Our evaluation focuses on key metrics, including training duration, model accuracy, and total model size. This research demonstrates the promising potential of quantum machine learning in enhancing machine learning tasks and solving practical machine learning challenges available today.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2023 reference point for readers tracking recent quantum research.
  • The research explores the potential of quantum deep learning models to address challenging machine learning problems that classical deep learning models find difficult to tackle.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2312.01024 [2] arXiv https://arxiv.org/abs/2312.01024 [3] Publisher https://arxiv.org/abs/2312.01024

Local Citation Graph (Related-Paper Links)

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External citation index: OpenAlex citation signal

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