论文标题
对国家发电的等级调整弱测量的表现
Manifestation of Rank-Tuned Weak Measurements Towards Featured State Generation
论文作者
论文摘要
我们建议,基于UNSHAP测量的过程,可以从纠缠的初始状态中生成真正的多部分纠缠,而Qubits数量较少,可以通过两种方式进行分类 - 偏见和无偏的通货膨胀协议。在有偏见的情况下,优化了单个测量结果后获得的真正多部分纠缠(GME),从而优化了单个测量结果后获得的状态,从而在无偏见的情况下,在所有可能的结果上优化了平均GME,从而产生了具有高GME的状态。有趣的是,我们表明,根据GME度量,广义几何测量,基于一夫一妻制的纠缠度量,缠结和鲁棒性,针对因UNSHARP测量器的等级而定量化的粒子损失,根据GME度量,广义几何测量,基于一夫一妻制的纠缠度量,缠结和稳健性,根据GME度量,基于一夫一妻制的纠缠度量,缠结和鲁棒性产生了不同特征的多部分状态,这是有趣的。具体而言,在生产三量纯状态的过程中,我们证明排名$ 2 $的测量值只能创建Greenberger Horne Zeilinger(GHz)阶级状态,而仅生产W级级别的$ 4 $衡量标准,尽管排名-3 $ $ 3 $测量能够同时产生两者。对于具有任意数量量子数的多部分状态的情况,我们报告说,真正的多部分纠缠的平均内容随着测量运算符的等级的下降而增加,尽管持久性随级别的持续性降低,既在有偏见的情况下又是无偏置的协议。
We propose that an unsharp measurement-based process to generate genuine multipartite entanglement from an entangled initial state with a fewer number of qubits can be classified in two ways -- biased and unbiased inflation protocols. In the biased case, genuine multipartite entanglement (GME) of the resulting state obtained after a single measurement outcome is optimized, thereby creating a possibility of states with high GME while in the unbiased case, average GME is optimized over all possible outcomes. Interestingly, we show that the set of two-qubit unsharp measurements can generate multipartite states having different features according to GME measure, generalized geometric measure, the monogamy-based entanglement measure, tangle and robustness against particle loss quantified via persistency depending on the rank of the unsharp measurement operators. Specifically, in the process of producing three-qubit pure states, we prove that rank-$2$ measurements can create only Greenberger Horne Zeilinger (GHZ)-class states while only W-class states are produced with rank-$4$ measurements although rank-$3$ measurements are capable to generate both. In the case of multipartite states with an arbitrary number of qubits, we report that the average content of genuine multipartite entanglement increases with the decrease of the rank in the measurement operators although the persistency decreases with the rank, both in the biased as well as unbiased protocols.