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Quantum image compression with autoencoders based on parameterized quantum circuits

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Abstract

The analysis and processing of digital images play a vital role in information processing. However, the pixel-based operations on images often lead to significant complexity as the image data grow rapidly. Encoding images into a quantum system and leveraging the principles of superposition and entanglement offer a chance to alleviate the challenges. A further improvement in efficiency is promising by combining quantum image processing with machine learning algorithms. Here a quantum autoencoder is trained to compress the image data into a lower-dimensional space using a hybrid quantum-classical control approach. The optimization of the parameterized quantum circuit involves the measurement of simple observables, alleviating the computational burden associated with the calculation of cost functions and gradients. We applied our quantum autoencoder to compress the MNIST handwritten digit dataset. The results exhibit the feasibility and effectiveness of the quantum compression approach. This work highlights the potential application of quantum neural networks in achieving high-efficiency quantum image processing.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grants No. 12075206 and No. 11905184).

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Authors and Affiliations

  1. Department of Physics, Zhejiang University of Science and Technology, Hangzhou, 310023, China

    Hengyan Wang, Jing Tan & Yixiao Huang

  2. Zhejiang Provincial Key Laboratory and Collaborative Innovation Center for Quantum Precision Measurement, College of Science, Zhejiang University of Technology, Hangzhou, 310023, China

    Wenqiang Zheng

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  1. Hengyan Wang
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Correspondence to Wenqiang Zheng.

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Wang, H., Tan, J., Huang, Y. et al. Quantum image compression with autoencoders based on parameterized quantum circuits. Quantum Inf Process 23, 41 (2024). https://doi.org/10.1007/s11128-023-04243-3

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