Quantum entanglement plays an important role in quantum information processes, such as quantum computation and quantum communication. Experiments in laboratories are unquestionably crucial to increase our understanding of quantum systems and inspire new insights into future applications. However, there are no general recipes for the creation of arbitrary quantum states with many particles entangled in high dimensions. Here we exploit a recent connection between quantum experiments and graph theory and answer this question for a plethora of classes of entangled states. We find experimental setups for Greenberger-Horne-Zeilinger states, $W$ states, general Dicke states, and asymmetrically high-dimensional multipartite entangled states. This result sheds light on the producibility of arbitrary quantum states using photonic technology with probabilistic pair sources and allows us to understand the underlying technological and fundamental properties of entanglement.

The weighted K‐nearest neighbour (WKNN) algorithm is widely applied to fingerprint positioning. However, the node position estimated by the WKNN algorithm is not optimal in a noisy environment. To obtain the optimised node location estimate, the authors propose an optimal WKNN (OWKNN) algorithm for wireless sensor network (WSN) fingerprint localisation in a noisy environment. The proposed OWKNN algorithm is composed of an adaptive Kalman filter (AKF) and a memetic algorithm (MA). First, the AKF is utilised to reduce the measurement noise of the received signal strength indication (RSSI) between the nodes in the WSN. Then, the MA is employed to optimise the calibration point weight for estimating the position of a target node in the WSN according to the filtered RSSI and a calibrated radio map. Finally, an optimal node location estimate is achieved based on the optimised weight. The extensive experimental results reveal that the localisation accuracy of the proposed algorithm is at least ∼50% higher than those of the state‐of‐the‐art fingerprint localisation algorithms regardless of the placement of the target node, number of beacon nodes, and size of the calibration cell.

We introduce the concept of hypergraphs to describe quantum optical experiments with probabilistic multiphoton sources. Every hyperedge represents a correlated photon source, and every vertex stands for an optical output path. Such a general graph description provides new insights for producing complex high-dimensional multiphoton quantum entangled states, which go beyond limitations imposed by pair creation via spontaneous parametric down-conversion. Furthermore, the properties of hypergraphs can be investigated experimentally. For example, the NP-complete problem of deciding whether a hypergraph has a perfect matching can be answered by experimentally detecting multiphoton events in quantum experiments. By introducing complex weights in hypergraphs, we show a general many-particle quantum interference and manipulating entanglement in a pictorial way. Our work paves the path for the development of multiphoton high-dimensional state generation and might inspire new applications of quantum computations using hypergraph mappings.

RFID tags have been widely deployed to report valuable information about tagged objects or surrounding environment. To collect such information, the key is to avoid the tag-to-tag collision in the open wireless channel. Polling, as a widely used anti-collision protocol, provides a request-response way to interrogate tags. The basic polling however needs to broadcast the tedious tag ID (96 bits) to query a tag, which is time-consuming. For example, collecting only 1-bit information (e.g., battery status) but with 96-bit overhead is a great limitation. This paper studies how to design efficient polling protocols to collect tag information quickly. The basic idea is to minimize the length of the polling vector as well as to avoid useless communication. We first propose an efficient Hash polling protocol (HPP) that uses hash indices rather than tag IDs as the polling vector to query each tag. The length of the polling vector is dropped from 96 bits to no more than 16 bits (the number of tags is less than 100,000). We then propose a tree-based polling protocol (TPP) that avoids redundant transmission in HPP. By constructing a binary polling tree, TPP transmits only different postfix of the neighbor polling vectors; the same prefix is reserved without any retransmission. The result is that the length of the polling vector reduces to only 3.4 bits. Finally, we propose an incremental polling protocol (IPP) that updates the polling vector based on the difference in value between the current polling vector and the previous one. By sorting the indices and dynamically updating them, IPP drops the polling vector to 1.6 bits long, 60 times less than 96-bit IDs. Extensive simulation results show that our best protocol IPP outperforms the state-of-the-art information collection protocol.

In RFID-enabled applications, when a tag is put into use and associated with a specific object, the category-related information (e.g., the brands of clothes) about this object might be preloaded into the tag's memory for the purpose of live query. Since such information reflects category attributes, all tags in the same category carry identical category information. To collect this information, we do not need to repeatedly interrogate each tag; one tag's response in a category is sufficient. In this paper, we investigate the problem of category information collection in a multi-category RFID system, which is referred to as information sampling. We propose two time-efficiency protocols. The first is a two-phase sampling protocol (TPS) that works in the case of knowing tag IDs. By quickly zooming into a category and isolating a tag from this category, TPS is able to sample a category with small overhead. The second protocol, called back-and-forth sampling protocol (BFS), relaxes a key assumption in TPS and performs the sampling task efficiently without knowing any tag IDs or category IDs. By carrying out a step-forward frame and using the step-backward scheme, BFS is able to interrogate only 1.45 tags (close to the lower bound of one tag) on average for each category. We theoretically analyze the protocol performance of TPS and BFS and discuss the optimal parameter settings that minimize the overall execution time. Extensive simulations show that both the protocols outperform the benchmark, greatly improving the sampling performance.

Counterfactual quantum communication allows distant users to share a message without any physical particles traveling through the quantum channel. Here, we present a counterfactual scheme to realize multiparty entanglement distribution. We first realize bipartite counterfactual entanglement distribution to create photon–electron entanglement by using interaction-free measurements and quantum dots. Then the atom–cavity systems are used to create many photon–photon entanglement pairs, which can connect photon–electron pairs with the help of an entanglement beam splitter. Consequently, the electron spins in distant optical microcavities are entangled by the coherent exchange of single flying photons. We also discuss the implementation issues to show that the presented scheme can be realized successfully in experiment. The numerical analysis about the efficiency of multiparty entanglement distribution indicates that there is a trade-off between the entanglement fidelity and the number of users.