Quantum image compression with autoencoders based on parameterized quantum circuits - Wang, Hengyan; Tan, Jing; Huang, Yixiao; Zheng, Wenqiang (Quantum information processing, 2024)
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.