Geometric discord of tripartite quantum systems

We study the quantification of geometric discord for tripartite quantum systems.Firstly,we obtain the analytic formula of geometric discord for tripartite pure states.It is already known that the geometric discord of pure states reduces to the geometric entanglement in bipartite systems,the results presented here show that this property is no longer true in tripartite systems.Furthermore,we provide an operational meaning for tripartite geometric discord by linking it to quantum state discrimination,that is,we prove that the geometric discord of tripartite states is equal to the minimum error probability to discriminate a set of quantum states with von Neumann measurement.Lastly,we calculate the geometric discord of three-qubit Bell diagonal states and then investigate the dynamic behavior of tripartite geometric discord under local decoherence.It is interesting that the frozen phenomenon exists for geometric discord in this scenario.

Protecting geometric quantum discord via partially collapsing measurements of two qubits in multiple bosonic reservoirs

We study the dynamics of geometric quantum discord(GQD) between two qubits,each qubit interacting at the same time with K independent multiple bosonic reservoirs at zero temperature.In both weak and strong qubit-reservoirs coupling regimes,we find that the increase of the number K of reservoirs can induce the damped oscillation of GQD,and enhance the memory effects of the overall environment.And the Hilbert-Schmidt norm GQD(two-norm GQD) is always smaller than the trace norm geometric quantum discord(one-norm GQD).Therefore,the one-norm GQD is a better way to measure the quantum correlation.Finally,we propose an effective strategy to improve GQD by using partially collapsing measurements,and we find that the protection effect is better with the increase of the weak measurement strength.

Research on the Freezing Phenomenon of Quantum Correlation by Machine Learning

Quantum correlation shows a fascinating nature of quantum mechanics and plays an important role in some physics topics,especially in the field of quantum information.Quantum correlations of the composite system can be quantified by resorting to geometric or entropy methods,and all these quantification methods exhibit the peculiar freezing phenomenon.The challenge is to find the characteristics of the quantum states that generate the freezing phenomenon,rather than only study the conditions which generate this phenomenon under a certain quantum system.In essence,this is a classification problem.Machine learning has become an effective method for researchers to study classification and feature generation.In this work,we prove that the machine learning can solve the problem of X form quantum states,which is a problem of physical significance.Subsequently,we apply the density-based spatial clustering of applications with noise(DBSCAN)algorithm and the decision tree to divide quantum states into two different groups.Our goal is to classify the quantum correlations of quantum states into two classes:one is the quantum correlation with freezing phenomenon for both Rènyi discord(α=2)and the geometric discord(Bures distance),the other is the quantum correlation of non-freezing phenomenon.The results demonstrate that the machine learning method has reasonable performance in quantum correlation research.