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 im...
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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.
quantum neural networks are emerging as potential candidates to leverage noisy quantum processing units for applications. Here we introduce hybrid quantum-classical autoencoders for end-to-end radio communication. In ...
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ISBN:
(纸本)9781665486118
quantum neural networks are emerging as potential candidates to leverage noisy quantum processing units for applications. Here we introduce hybrid quantum-classical autoencoders for end-to-end radio communication. In the physical layer of classical wireless systems, we study the performance of simulated architectures for standard encoded radio signals over a noisy channel. We implement a hybrid model, where a quantum decoder in the receiver works with a classical encoder in the transmitter part. Besides learning a latent space representation of the input symbols with good robustness against signal degradation, a generalized data re-uploading scheme for the qubit-based circuits allows to meet inference-time constraints of the application.
The quantum autoencoder is a recent paradigm in the field of quantum machine learning, which may enable an enhanced use of resources in quantum technologies. To this end, quantum neural networks with less nodes in the...
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The quantum autoencoder is a recent paradigm in the field of quantum machine learning, which may enable an enhanced use of resources in quantum technologies. To this end, quantum neural networks with less nodes in the inner than in the outer layers were considered. Here, we propose a useful connection between quantum autoencoders and quantum adders, which approximately add two unknown quantum states supported in different quantum systems. Specifically, this link allows us to employ optimized approximate quantum adders, obtained with genetic algorithms, for the implementation of quantum autoencoders for a variety of initial states. Furthermore, we can also directly optimize the quantum autoencoders via genetic algorithms. Our approach opens a different path for the design of quantum autoencoders in controllable quantum platforms.
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