Related Experiment Video
Updated: Mar 22, 2026

12:19
Measurement of Quantum Interference in a Silicon Ring Resonator Photon Source
Published on: April 4, 2017
8.9K
Grounding the randomness of quantum measurement
1Quantum Communication and Measurement Laboratory, Department of Electrical and Computer Engineering, Division of Natural Science and Mathematics, Boston University, Boston, MA 02215, USA gsjaeger@gmail.com.
Summary
Julian Schwinger
Area of Science:
- Quantum mechanics
- Mathematical physics
Background:
- Julian Schwinger developed a mathematical framework for quantum mechanics based on measurement sequences.
- This framework utilizes an algebra of symbols representing discrete quantum measurements and their outcomes.
Purpose of the Study:
- To evaluate Schwinger's assumption of random phase parameters in quantum mechanics.
- To ground the objective probabilities in quantum mechanics by relating phase randomness to the principle of plenitude.
Main Methods:
- Examining Schwinger's algebraic reconstruction of quantum mechanics.
- Analyzing recent critiques of randomness.
- Connecting the principle of plenitude with the randomness of phase parameters.
Main Results:
- Schwinger's assumption of random phase parameters is evaluated against contemporary understandings of randomness.
- A link is established between the "principle of plenitude" and the randomness of phase parameters.
- This provides a foundational basis for quantum mechanical probabilities.
Conclusions:
- The study provides a fundamental grounding for the objective, irreducible probabilities in quantum mechanics.
- It reconciles Schwinger's model with modern critiques of randomness.
- The principle of plenitude offers a basis for the definite measured values observed in quantum mechanics.
Related Concept Videos
The Uncertainty Principle
34.5K
Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
34.5K
Random Error
10.0K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
10.0K
Uncertainty in Measurement: Accuracy and Precision
113.5K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
113.5K
Random and Systematic Errors
16.0K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
16.0K
Random and Systematic Errors
926
926
Random Variables
18.7K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
18.7K

