Related Experiment Video
Updated: Aug 4, 2026
12:19
Measurement of Quantum Interference in a Silicon Ring Resonator Photon Source
Published on: April 4, 2017
Proposal for the measurement of bell-type correlations from continuous variables
1Department of Physics, Centre for Laser Science, University of Queensland, Queensland 4072, Australia.
Physical Review Letters
|September 6, 2000
Summary
Theoretical analysis reveals that Bell-type correlations emerge from continuous variable measurements on a parametric source. Auxiliary measurements in the detection environment rule out local realistic explanations for these quantum correlations.
Area of Science:
- Quantum Information Science
- Quantum Optics
- Foundations of Physics
Background:
- Bell-type correlations challenge local realism, a cornerstone of classical physics.
- Parametric sources are crucial for generating entangled quantum states.
- Continuous variable measurements are key to exploring quantum phenomena.
Purpose of the Study:
- To theoretically demonstrate Bell-type correlations in continuous variable systems.
- To investigate the role of auxiliary measurements in quantum correlations.
- To challenge local realistic descriptions of quantum phenomena.
Main Methods:
- Theoretical modeling of a parametric source.
- Analysis of continuous variable measurements.
- Incorporation of auxiliary measurements in the detection environment.
Main Results:
- Observation of Bell-type correlations between continuous variable measurements.
- Demonstration that auxiliary measurements preclude local realistic explanations.
- Theoretical validation of quantum correlations beyond classical intuition.
Conclusions:
- Continuous variable systems exhibit non-classical correlations.
- Quantum correlations observed are incompatible with local realism.
- The study provides theoretical insights into the foundations of quantum mechanics.
Related Concept Videos
02:20Correlations
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...
01:09Correlation
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
01:11Calculating and Interpreting the Linear Correlation Coefficient
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
01:10Calibration Curves: Correlation Coefficient
In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the other increases, and...
00:53Correlation and Regression
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a negative...
01:23Correlation of Experimental Data
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity, and...
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity, and...