Quantum randomness introduced through squeezing operations and random number generation.

Quantum random numbers play a crucial role in diverse applications, including cryptography, simulation, and artificial intelligence. In contrast to predictable algorithm-based pseudo-random numbers, quantum physics provides new avenues for generating theoretically true random numbers by exploiting the inherent uncertainty contained in quantum phenomena. Here, we propose and demonstrate a quantum random number generator (QRNG) using a prepared broadband squeezed state of light, where the randomness of the generated numbers entirely originates from the quantum noise introduced by squeezing operation rather than vacuum noise. The relationship between entropy rate and squeezing level is analyzed. Furthermore, we employ a source-independent quantum random number protocol to enhance the security of the random number generator.

Remote and controlled quantum teleportation network of the polarization squeezed state.

Quantum teleportation is a building block in quantum computation and quantum communication. The continuous-variable polarization squeezed state is a key resource in quantum networks, offering advantages for long-distance distribution and direct interfacing of quantum nodes. Although polarization squeezed state has been generated and distributed between remote users, it is a long-standing goal to implement controlled quantum teleportation of the polarization squeezed state with multiple remote users. Here, we propose a feasible scheme to teleport a polarization squeezed state among multiple remote users under control. The polarization state is transferred between different remote quantum networks, and the controlled quantum teleportation of the polarization state can be implemented in one quantum network involving multiple remote users. The results show that such a controlled quantum teleportation can be realized with 36 users through about 6-km free-space or fiber quantum channels, where the fidelity of 0.352 is achieved beyond the classical limit of 0.349 with an input squeezing variance of 0.25. This scheme provides a direct reference for the experimental implementation of remote and controlled quantum teleportation of polarization states, thus enabling more teleportation-based quantum network protocols.

High-Speed Quantum Radio-Frequency-Over-Light Communication.

Quantum dense coding (QDC) means to transmit two classical bits by only transferring one quantum bit, which has enabled high-capacity information transmission and strengthened system security. Continuous-variable QDC offers a promising solution to increase communication rates while achieving seamless integration with classical communication systems. Here, we propose and experimentally demonstrate a high-speed quantum radio-frequency-over-light (RFOL) communication scheme based on QDC with an entangled state, and achieve a practical rate of 20 Mbps through digital modulation and RFOL communication. This scheme bridges the gap between quantum technology and real-world communication systems, which bring QDC closer to practical applications and offer prospects for further enhancement of metropolitan communication networks.

HSG-MGAF Net: Heterogeneous subgraph-guided multiscale graph attention fusion network for interpretable prediction of whole-slide image.

BACKGROUND AND OBJECTIVE: Pathological whole slide image (WSI) prediction and region of interest (ROI) localization are important issues in computer-aided diagnosis and postoperative analysis in clinical applications. Existing computer-aided methods for predicting WSI are mainly based on multiple instance learning (MIL) and its variants. However, most of the methods are based on instance independence and identical distribution assumption and performed at a single scale, which not fully exploit the hierarchical multiscale heterogeneous information contained in WSI. METHODS: Heterogeneous Subgraph-Guided Multiscale Graph Attention Fusion Network (HSG-MGAF Net) is proposed to build the topology of critical image patches at two scales for adaptive WSI prediction and lesion localization. The HSG-MGAF Net simulates the hierarchical heterogeneous information of WSI through graph and hypergraph at two scales, respectively. This framework not only fully exploits the low-order and potential high-order correlations of image patches at each scale, but also leverages the heterogeneous information of the two scales for adaptive WSI prediction. RESULTS: We validate the superiority of the proposed method on the CAMELYON16 and the TCGA- NSCLC, and the results show that HSG-MGAF Net outperforms the state-of-the-art method on both datasets. The average ACC, AUC and F1 score of HSG-MGAF Net can reach 92.7 %/0.951/0.892 and 92.2 %/0.957/0.919, respectively. The obtained heatmaps can also localize the positive regions more accurately, which have great consistency with the pixel-level labels. CONCLUSIONS: The results demonstrate that HSG-MGAF Net outperforms existing weakly supervised learning methods by introducing critical heterogeneous information between the two scales. This approach paves the way for further research on light weighted heterogeneous graph-based WSI prediction and ROI localization.

Improving Fast Adversarial Training With Prior-Guided Knowledge.

Fast adversarial training (FAT) is an efficient method to improve robustness in white-box attack scenarios. However, the original FAT suffers from catastrophic overfitting, which dramatically and suddenly reduces robustness after a few training epochs. Although various FAT variants have been proposed to prevent overfitting, they require high training time. In this paper, we investigate the relationship between adversarial example quality and catastrophic overfitting by comparing the training processes of standard adversarial training and FAT. We find that catastrophic overfitting occurs when the attack success rate of adversarial examples becomes worse. Based on this observation, we propose a positive prior-guided adversarial initialization to prevent overfitting by improving adversarial example quality without extra training time. This initialization is generated by using high-quality adversarial perturbations from the historical training process. We provide theoretical analysis for the proposed initialization and propose a prior-guided regularization method that boosts the smoothness of the loss function. Additionally, we design a prior-guided ensemble FAT method that averages the different model weights of historical models using different decay rates. Our proposed method, called FGSM-PGK, assembles the prior-guided knowledge, i.e., the prior-guided initialization and model weights, acquired during the historical training process. The proposed method can effectively improve the model's adversarial robustness in white-box attack scenarios. Evaluations of four datasets demonstrate the superiority of the proposed method.

Evaluating the impact of China's MSW sorting pilot policy on urban sustainable development: Empirical evidence from 95 cities.

The escalating challenge of municipal solid waste (MSW) critically tests the sustainable development capacities of urban centers. In response, China initiated pilot policies in 2017 aimed at bolstering MSW management. The effectiveness of these initiatives, however, necessitates empirical scrutiny. This study leverages panel data spanning 95 cities at the prefectural level or higher, covering the period from 2006 to 2020, to assess the impact of the MSW sorting pilot policy on urban sustainable development using a difference-in-differences approach. The research found that the MSW sorting pilot policy has significantly increased the processing volume of MSW, thereby enhancing the sustainable development capabilities of cities. Further, the study identifies augmented fixed asset investments as a key mechanism through which pilot cities have enhanced their MSW management capabilities. Notably, the policy's stimulative effects are more pronounced in less densely populated and economically lagging regions. These findings provide critical insights for developing nations in shaping MSW sorting strategies and advancing urban sustainability.