Providing secure and efficient transmission for multiple optical images has been an important issue in the field of information security. Here we present a hybrid image compression, encryption and reconstruction scheme based on deep learning-assisted single-pixel imaging (SPI) and orthogonal coding. In the optical SPI-based encryption, two-dimensional images are encrypted into one-dimensional bucket signals, which will be further compressed by a binarization operation. By overlaying orthogonal coding on the compressed signals, we obtain the ciphertext that allows multiple users to access with the same privileges. The ciphertext can be decoded back to the binarized bucket signals with the help of orthogonal keys. To enhance reconstruction efficiency and quality, a deep learning framework based on DenseNet is employed to retrieve the original optical images. Numerical and experimental results have been presented to verify the feasibility and effectiveness of the proposed scheme.
Abstract We used deep learning techniques to construct various models for reconstructing quantum states from a given set of coincidence measurements. Through simulations, we have demonstrated that our approach generates functionally equivalent reconstructed states for a wide range of pure and mixed input states. Compared with traditional methods, our system offers the advantage of faster speed. Additionally, by training our system with measurement results containing simulated noise sources, the system shows a significant improvement in average fidelity compared with typical reconstruction methods. We also found that constraining the variational manifold to physical states, i.e., positive semi-definite density matrices, greatly enhances the quality of the reconstructed states in the presence of experimental imperfections and noise. Finally, we validated the correctness and superiority of our model by using data generated on IBM Quantum Platform, a real quantum computer.
Negativities in quasiprobability distributions, a foundational concept originating in quantum optics, serve as a fundamental signature of quantum nonclassicality, with entanglement quasiprobabilities offering a necessary and sufficient criterion for entanglement. However, practical reconstruction of entanglement quasiprobabilities conventionally requires full quantum state tomography, severely limiting scalability. Here, we propose a deep-learning framework that reconstructs entanglement quasiprobabilities directly from incomplete local projective measurements, bypassing full state reconstruction. Using a residual neural network, partial measurement outcomes are mapped to high-fidelity entanglement quasiprobabilities. Numerical benchmarks up to three qubits show more than a
30× reduction in reconstruction error compared with state-of-the-art tomographic methods. Experimental validation on photonic entangled states demonstrates reconstruction and entanglement detection with substantially reduced measurement resources. Our results establish machine-learning-assisted reconstruction of entanglement quasiprobabilities as a scalable and practical tool for entanglement characterization in quantum optical systems.