Holographic Storage of Biphoton Entanglement

Coherent and reversible storage of multi-photon entanglement with a multimode quantum memory is essential for scalable all-optical quantum information processing. Although single photon has been successfully stored in different quantum systems, storage of multi-photon entanglement remains challenging because of the critical requirement for coherent control of photonic entanglement source, multimode quantum memory, and quantum interface between them. Here we demonstrate a coherent and reversible storage of biphoton Bell-type entanglement with a holographic multimode atomic-ensemble-based quantum memory. The retrieved biphoton entanglement violates Bell's inequality for 1 microsecond storage time and a memory-process fidelity of 98% is demonstrated by quantum state tomography.Comment: 5 pages, 4 figures, accepted by Phys. Rev. Let

Learning Multiplex Embeddings on Text-rich Networks with One Text Encoder

In real-world scenarios, texts in a network are often linked by multiple semantic relations (e.g., papers in an academic network are referenced by other publications, written by the same author, or published in the same venue), where text documents and their relations form a multiplex text-rich network. Mainstream text representation learning methods use pretrained language models (PLMs) to generate one embedding for each text unit, expecting that all types of relations between texts can be captured by these single-view embeddings. However, this presumption does not hold particularly in multiplex text-rich networks. Along another line of work, multiplex graph neural networks (GNNs) directly initialize node attributes as a feature vector for node representation learning, but they cannot fully capture the semantics of the nodes' associated texts. To bridge these gaps, we propose METERN, a new framework for learning Multiplex Embeddings on TExt-Rich Networks. In contrast to existing methods, METERN uses one text encoder to model the shared knowledge across relations and leverages a small number of parameters per relation to derive relation-specific representations. This allows the encoder to effectively capture the multiplex structures in the network while also preserving parameter efficiency. We conduct experiments on nine downstream tasks in five networks from both academic and e-commerce domains, where METERN outperforms baselines significantly and consistently. The code is available at https://github.com/PeterGriffinJin/METERN-submit.Comment: 9 pages, 11 appendix page

No-compressing of quantum phase information

We raise a general question of quantum information theory whether the quantum phase information can be compressed and retrieved. A general qubit contains both amplitude and phase information, while an equatorial qubit contains only a phase information. We study whether it is possible to compress the phase information of n equatorial qubits into m general qubits with m being less than n, and still those information can be retrieved perfectly. We prove that this process is not allowed by quantum mechanics.Comment: 4 pages, 1 figur

DiffULD: Diffusive Universal Lesion Detection

Universal Lesion Detection (ULD) in computed tomography (CT) plays an essential role in computer-aided diagnosis. Promising ULD results have been reported by anchor-based detection designs, but they have inherent drawbacks due to the use of anchors: i) Insufficient training targets and ii) Difficulties in anchor design. Diffusion probability models (DPM) have demonstrated outstanding capabilities in many vision tasks. Many DPM-based approaches achieve great success in natural image object detection without using anchors. But they are still ineffective for ULD due to the insufficient training targets. In this paper, we propose a novel ULD method, DiffULD, which utilizes DPM for lesion detection. To tackle the negative effect triggered by insufficient targets, we introduce a novel center-aligned bounding box padding strategy that provides additional high-quality training targets yet avoids significant performance deterioration. DiffULD is inherently advanced in locating lesions with diverse sizes and shapes since it can predict with arbitrary boxes. Experiments on the benchmark dataset DeepLesion show the superiority of DiffULD when compared to state-of-the-art ULD approaches