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Hong-Ou-Mandel effect is an important demonstration of particle indistinguishability, when identical single photons interfere at a beamsplitter to generate the two-photon entangled NOON state. On the other hand, NOON states with $N\ge3$ photons have long been conjectured beyond the deterministic generation of photon interference. To characterize the separation, we introduce the notion of genuine non-Gaussian entanglement (NGE), which cannot be generated via a generalized Hong-Ou-Mandel experiment, with Gaussian protocols extending the beamsplitter and separable input states replacing the single photons. We establish a resource theory to characterize such quantum correlations beyond Hong-Ou-Mandel and prove that NOON states with $N\ge 3$ are indeed among the NGE class. With the generalized Hong-Ou-Mandel protocol as free operations, we introduce two monotones to characterize genuine non-Gaussian entanglement: one derived from the entanglement entropy and the other from the minimal extension size required to convert a state into a free state. Finally, we demonstrate that the tomography process of pure free states can be performed efficiently, while all learning protocols of states with genuine non-Gaussian entanglement require exponential overheads connected to the monotone. This implies that states generated in Boson sampling are efficiently learnable despite its measurement statistics being hard to sample from.

The emergence of quantum sensor networks has presented opportunities for enhancing complex sensing tasks, while simultaneously introducing significant challenges in designing and analyzing quantum sensing protocols due to the intricate nature of entanglement and physical processes. Supervised learning assisted by an entangled sensor network (SLAEN) [Phys. Rev. X 9, 041023 (2019)] represents a promising paradigm for automating sensor-network design through variational quantum machine learning. However, the original SLAEN, constrained by the Gaussian nature of quantum circuits, is limited to learning linearly separable data. Leveraging the universal quantum control available in cavity-QED experiments, we propose a generalized SLAEN capable of handling nonlinear data classification tasks. We establish a theoretical framework for physical-layer data classification to underpin our approach. Through training quantum probes and measurements, we uncover a threshold phenomenon in classification error across various tasks -- when the energy of probes exceeds a certain threshold, the error drastically diminishes to zero, providing a significant improvement over the Gaussian SLAEN. Despite the non-Gaussian nature of the problem, we offer analytical insights into determining the threshold and residual error in the presence of noise. Our findings carry implications for radio-frequency photonic sensors and microwave dark matter haloscopes., Comment: 13 pages, 9 figures

Given a quantum system $S$ entangled with another system $I$, the entanglement testing problem arises, prompting the identification of the system $S$ within a set of $m \ge 2$ identical systems. This scenario serves as a model for the measurement task encountered in quantum ranging and entanglement-assisted communication [Phys. Rev. Lett. 126, 240501, (2021)]. In this context, the optimal measurement approach typically involves joint measurements on all $m+1$ systems. However, we demonstrate that this is not the case when the subsystems containing system $S$ are subjected to entanglement-breaking noise. Our approach utilizes the recently developed measurement technique of correlation-to-displacement conversion. We present a structured design for the entanglement testing measurement, implementable with local operations and classical communications (LOCC) on the $m+1$ systems. Furthermore, we prove that this measurement approach achieves optimality in terms of error probability asymptotically under noisy conditions. When applied to quantum illumination, our measurement design enables optimal ranging in scenarios with low signal brightness and high levels of noise. Similarly, when applied to entanglement-assisted classical communication, the measurement design leads to a significant relative advantage in communication rates, particularly in scenarios with low signal brightness., Comment: 10 pages, 5 figures

Deciding if a given family of quantum states is topologically ordered is an important but nontrivial problem in condensed matter physics and quantum information theory. We derive necessary and sufficient conditions for a family of graph states to be in TQO-1, which is a class of quantum error correction code states whose code distance scales macroscopically with the number of physical qubits. Using these criteria, we consider a number of specific graph families, including the star and complete graphs, and the line graphs of complete and completely bipartite graphs, and discuss which are topologically ordered and how to construct the codewords. The formalism is then employed to construct several codes with macroscopic distance, including a three-dimensional topological code generated by local stabilizers that also has a macroscopic number of encoded logical qubits. The results indicate that graph states provide a fruitful approach to the construction and characterization of topological stabilizer quantum error correction codes., Comment: 21 pages, 7 figures

Perceptrons, which perform binary classification, are the fundamental building blocks of neural networks. Given a data set of size~$N$ and margin~$\gamma$ (how well the given data are separated), the query complexity of the best-known quantum training algorithm scales as either $(\nicefrac{\sqrt{N}}{\gamma^2})\log(\nicefrac1{\gamma^2)}$ or $\nicefrac{N}{\sqrt{\gamma}}$, which is achieved by a hybrid of classical and quantum search. In this paper, we improve the version space quantum training method for perceptrons such that the query complexity of our algorithm scales as $\sqrt{\nicefrac{N}{\gamma}}$. This is achieved by constructing an oracle for the perceptrons using quantum counting of the number of data elements that are correctly classified. We show that query complexity to construct such an oracle has a quadratic improvement over classical methods. Once such an oracle is constructed, bounded-error quantum search can be used to search over the hyperplane instances. The optimality of our algorithm is proven by reducing the evaluation of a two-level AND-OR tree (for which the query complexity lower bound is known) to a multi-criterion search. Our quantum training algorithm can be generalized to train more complex machine learning models such as neural networks, which are built on a large number of perceptrons., Comment: 9 pages, 3 figures

Given their potential for fault-tolerant operations, topological quantum states are currently the focus of intense activity. Of particular interest are topological quantum error correction codes, such as the surface and planar stabilizer codes that are equivalent to the celebrated toric code. While every stabilizer state maps to a graph state under local Clifford operations, the graphs associated with topological stabilizer codes remain unknown. We show that the toric code graph is composed of only two kinds of subgraphs: star graphs (which encode Greenberger-Horne-Zeilinger states) and half graphs. The topological order is identified with the existence of multiple star graphs, which reveals a connection between the repetition and toric codes. The graph structure readily yields a log-depth quantum circuit for state preparation, assuming geometrically non-local gates, which can be reduced to a constant depth including ancillae and measurements at the cost of increasing the circuit width. The results provide a new graph-theoretic framework for the investigation of topological order and the development of novel topological error correction codes., Comment: 18 pages, 8 figures. One section about encoding arbitrary state into toric code is added

Recent neural sequence to sequence models have provided feasible solutions for abstractive summarization. However, such models are still hard to tackle long text dependency in the summarization task. A high-quality summarization system usually depends on strong encoder which can refine important information from long input texts so that the decoder can generate salient summaries from the encoder's memory. In this paper, we propose an aggregation mechanism based on the Transformer model to address the challenge of long text representation. Our model can review history information to make encoder hold more memory capacity. Empirically, we apply our aggregation mechanism to the Transformer model and experiment on CNN/DailyMail dataset to achieve higher quality summaries compared to several strong baseline models on the ROUGE metrics.