Hybrid Quantum Neural Network in High-dimensional Data Classification

Hao-Yuan Chen, Yen-Jui Chang, Shih-Wei Liao, Ching-Ray Chang
12/2/2023
cs.LGcs.AIquant-ph

No official code found. Best implementation: Spiegeldondi/A-Hybrid-Quantum-Classical-Framework-for-Reinforcement-Learning-of-Atari-Games (0 stars, Apache-2.0, last commit 2025-03-25).

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.

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Code Implementations(1)

Apache-2.0

This repo explores the capabilities of hybrid quantum-classical models based on variational quantum algorithms (VQAs) in high dimensional reinforcement learning (RL) environments such as the Atari 2600 classics Pong and Breakout.

00PythonMay 5, 20241 years agoApache-2.0

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Cite this paper

@article{chen2023hybrid,
  title  = {Hybrid Quantum Neural Network in High-dimensional Data Classification},
  author = {Hao-Yuan Chen and Yen-Jui Chang and Shih-Wei Liao and Ching-Ray Chang},
  year   = {2023},
  eprint = {2312.01024},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2312.01024v1}
}

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