Related Experiment Video
Updated: Feb 15, 2026
11:22
Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
Published on: January 30, 2018
10.7K
Majorana zero modes and long range edge correlation in interacting Kitaev chains: analytic solutions and
Jian-Jian Miao1,2, Hui-Ke Jin1,2, Fu-Chun Zhang1,2,3
1Department of Physics, Zhejiang University, Hangzhou, 310013, P. R. China.
Scientific Reports
|January 12, 2018
Summary
We investigate the one-dimensional Kitaev model, exploring topological superconductivity using analytical and numerical methods. An edge correlation function of Majorana fermions is proposed to detect long-range order in these exotic states.
Area of Science:
- Condensed Matter Physics
- Quantum Many-Body Systems
Background:
- The Kitaev model describes exotic topological superconducting states.
- Understanding edge properties is crucial for characterizing topological order.
Purpose of the Study:
- To investigate the one-dimensional Kitaev model with open boundary conditions.
- To propose and analyze an edge correlation function for Majorana fermions.
- To map the phase diagram of the interacting Kitaev chain.
Main Methods:
- Exact analytical methods for non-interacting systems.
- Density-matrix renormalization-group (DMRG) for interacting systems.
- Analysis of Majorana fermion edge correlation functions.
Main Results:
- Characterization of topological superconducting states.
- Identification of long-range order via Majorana fermion edge correlations.
- Determination of the phase diagram for the interacting Kitaev chain.
Conclusions:
- The proposed edge correlation function effectively detects topological order.
- The study provides insights into the phase diagram and properties of the interacting Kitaev chain.
Related Concept Videos
02:20Correlations
36.6K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
36.6K
01:24Overview of Cell-Matrix Interactions
9.2K
The extracellular matrix or ECM holds cells together to form a tissue and allows the cells within the tissue to communicate. ECM comprises proteins such as fibronectin, collagen, laminin, etc. The most abundant protein in this space is collagen. Collagen fibers are interwoven with carbohydrate-containing protein molecules called proteoglycans. ECM allows cell migration and provides a structural scaffold at cell adhesion that anchors the cell when the extracellular matrix proteins interact with...
9.2K
01:28Electron Transport Chains
113.3K
The final stage of cellular respiration is oxidative phosphorylation that consists of two steps: the electron transport chain and chemiosmosis. The electron transport chain is a set of proteins found in the inner mitochondrial membrane in eukaryotic cells. Its primary function is to establish a proton gradient that can be used during chemiosmosis to produce ATP and generate electron carriers, such as NAD+ and FAD, that are used in glycolysis and the citric acid cycle.
The ETC is comprised of...
The ETC is comprised of...
113.3K
01:27Correlation and Causation
43.0K
Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
43.0K
00:59Range
14.5K
The range is one of the measures of variation. It can be defined as the difference between a dataset's highest and lowest values. For example, in the study of seven 16-ounce soda cans, the filled volume of soda was measured, thus producing the following amount (in ounces) of soda:
15.9; 16.1; 15.2; 14.8; 15.8; 15.9; 16.0; 15.5
Measurements of the amount of soda in a 16-ounce can vary since different subjects record these measurements or since the exact amount - 16 ounces of liquid, was not...
15.9; 16.1; 15.2; 14.8; 15.8; 15.9; 16.0; 15.5
Measurements of the amount of soda in a 16-ounce can vary since different subjects record these measurements or since the exact amount - 16 ounces of liquid, was not...
14.5K
01:07What is a Mode?
26.8K
The mode is one of the commonly used measures of a central tendency. It is defined as the most frequent value in a data set.
There can be more than one mode in a data set if multiple values have the same highest frequency. For instance, suppose that the Statistics exam scores of 20 students are: 50; 53; 59; 59; 63; 63; 72; 72; 72; 72; 72; 76; 78; 81; 83; 84; 84; 84; 90; 93. Here, the mode is 72, as it occurs most frequently, five times.
A data set with two modes is called bimodal. For example,...
There can be more than one mode in a data set if multiple values have the same highest frequency. For instance, suppose that the Statistics exam scores of 20 students are: 50; 53; 59; 59; 63; 63; 72; 72; 72; 72; 72; 76; 78; 81; 83; 84; 84; 84; 90; 93. Here, the mode is 72, as it occurs most frequently, five times.
A data set with two modes is called bimodal. For example,...
26.8K