The phenomenon of phase separation into antiferromagnetic（AFM） and superconducting（SC） or normal-state regions has great implication for the origin of high-temperature（high-T_c） superconductivity. However, the o...The phenomenon of phase separation into antiferromagnetic（AFM） and superconducting（SC） or normal-state regions has great implication for the origin of high-temperature（high-T_c） superconductivity. However, the occurrence of an intrinsic antiferromagnetism above the T_c of（Li,Fe）OHFe Se superconductor is questioned. Here we report a systematic study on a series of（Li,Fe）OHFe Se single crystal samples with T_c up to ～41 K. We observe an evident drop in the static magnetization at T_（afm） ～ 125 K, in some of the SC（T_c 38 K, cell parameter c■9.27 ？） and non-SC samples. We verify that this AFM signal is intrinsic to（Li,Fe）OHFe Se. Thus, our observations indicate mesoscopic-to-macroscopic coexistence of an AFM state with the normal（below T_（afm）） or SC（below T_c） state in（Li,Fe）OHFe Se. We explain such coexistence by electronic phase separation, similar to that in high-T_c cuprates and iron arsenides. However, such an AFM signal can be absent in some other samples of（Li,Fe）OHFe Se, particularly it is never observed in the SC samples of T_c 38 K, owing to a spatial scale of the phase separation too small for the macroscopic magnetic probe. For this case, we propose a microscopic electronic phase separation. The occurrence of two-dimensional AFM spin fluctuations below nearly the same temperature as T_（afm）, reported previously for a（Li,Fe）OHFe Se（T_c ～ 42 K） single crystal, suggests that the microscopic static phase separation reaches vanishing point in high T_c（Li,Fe）OHFe Se. A complete phase diagram is thus established. Our study provides key information of the underlying physics for high-T_c superconductivity.展开更多
The Hamiltonian cycle problem(HCP),which is an NP-complete problem,consists of having a graph G with n nodes and m edges and finding the path that connects each node exactly once.In this paper we compare some algorith...The Hamiltonian cycle problem(HCP),which is an NP-complete problem,consists of having a graph G with n nodes and m edges and finding the path that connects each node exactly once.In this paper we compare some algorithms to solve a Hamiltonian cycle problem,using different models of computations and especially the probabilistic and quantum ones.Starting from the classical probabilistic approach of random walks,we take a step to the quantum direction by involving an ad hoc designed Quantum Turing Machine(QTM),which can be a useful conceptual project tool for quantum algorithms.Introducing several constraints to the graphs,our analysis leads to not-exponential speedup improvements to the best-known algorithms.In particular,the results are based on bounded degree graphs(graphs with nodes having a maximum number of edges)and graphs with the right limited number of nodes and edges to allow them to outperform the other algorithms.展开更多
基金Supported by the National Key Research and Development Program of China under Grant Nos 2017YFA0303003,2016YFA0300300 and 2015CB921000the National Natural Science Foundation of China under Grant Nos 11574370,11474338,11674374 and 61501220+1 种基金the Strategic Priority Research Program and Key Research Program of Frontier Sciences of the Chinese Academy of Sciences under Grant Nos QYZDY-SSW-SLH001,QYZDY-SSW-SLH008 and XDB07020100the Beijing Municipal Science and Technology Project under Grant No Z161100002116011
文摘The phenomenon of phase separation into antiferromagnetic（AFM） and superconducting（SC） or normal-state regions has great implication for the origin of high-temperature（high-T_c） superconductivity. However, the occurrence of an intrinsic antiferromagnetism above the T_c of（Li,Fe）OHFe Se superconductor is questioned. Here we report a systematic study on a series of（Li,Fe）OHFe Se single crystal samples with T_c up to ～41 K. We observe an evident drop in the static magnetization at T_（afm） ～ 125 K, in some of the SC（T_c 38 K, cell parameter c■9.27 ？） and non-SC samples. We verify that this AFM signal is intrinsic to（Li,Fe）OHFe Se. Thus, our observations indicate mesoscopic-to-macroscopic coexistence of an AFM state with the normal（below T_（afm）） or SC（below T_c） state in（Li,Fe）OHFe Se. We explain such coexistence by electronic phase separation, similar to that in high-T_c cuprates and iron arsenides. However, such an AFM signal can be absent in some other samples of（Li,Fe）OHFe Se, particularly it is never observed in the SC samples of T_c 38 K, owing to a spatial scale of the phase separation too small for the macroscopic magnetic probe. For this case, we propose a microscopic electronic phase separation. The occurrence of two-dimensional AFM spin fluctuations below nearly the same temperature as T_（afm）, reported previously for a（Li,Fe）OHFe Se（T_c ～ 42 K） single crystal, suggests that the microscopic static phase separation reaches vanishing point in high T_c（Li,Fe）OHFe Se. A complete phase diagram is thus established. Our study provides key information of the underlying physics for high-T_c superconductivity.
基金the project PNRR-HPC,Big Data and Quantum Computing–CN1 Spoke 10,CUP I53C22000690001.
文摘The Hamiltonian cycle problem(HCP),which is an NP-complete problem,consists of having a graph G with n nodes and m edges and finding the path that connects each node exactly once.In this paper we compare some algorithms to solve a Hamiltonian cycle problem,using different models of computations and especially the probabilistic and quantum ones.Starting from the classical probabilistic approach of random walks,we take a step to the quantum direction by involving an ad hoc designed Quantum Turing Machine(QTM),which can be a useful conceptual project tool for quantum algorithms.Introducing several constraints to the graphs,our analysis leads to not-exponential speedup improvements to the best-known algorithms.In particular,the results are based on bounded degree graphs(graphs with nodes having a maximum number of edges)and graphs with the right limited number of nodes and edges to allow them to outperform the other algorithms.
