Related Experiment Video
Updated: Feb 26, 2026

12:19
Measurement of Quantum Interference in a Silicon Ring Resonator Photon Source
Published on: April 4, 2017
8.9K
Influence of measurement error on Maxwell's demon.
Vegard Sørdal1, Joakim Bergli1, Y M Galperin1,2
1Department of Physics, University of Oslo, 0316 Oslo, Norway.
Physical Review. E
|July 16, 2017
Summary
Measurement errors in thermodynamic cycles decrease system entropy reduction. This study optimizes protocols for a single-electron box Szilard engine to manage errors and maximize power output despite irreversibility.
Area of Science:
- Thermodynamics
- Statistical Mechanics
- Information Theory
Background:
- Thermodynamic cycles involve measurement, feedback, and erasure.
- Landauer's principle states erasure increases entropy.
- Measurement errors can lead to irreversible processes and increased entropy production.
Purpose of the Study:
- To analyze the impact of measurement errors on a single-electron box Szilard engine.
- To determine optimal operational protocols under realistic error conditions.
- To balance power output with entropy production in the presence of errors.
Main Methods:
- Theoretical analysis of a single-electron box Szilard engine model.
- Investigating the consequences of measurement errors on entropy and information.
- Deriving optimal control protocols as a function of power and error rate.
Main Results:
- Measurement errors reduce the entropy decrease associated with information gain.
- Erasure still contributes to entropy increase, making the overall process irreversible.
- Suboptimal feedback due to errors further escalates entropy production.
- An optimal protocol is identified to manage power and error.
Conclusions:
- Measurement errors fundamentally impact the reversibility and efficiency of thermodynamic cycles.
- Optimized protocols are crucial for mitigating negative effects of errors in nanoscale engines.
- This work provides a framework for designing robust nanoscale thermodynamic systems.
Related Concept Videos
Uncertainty in Measurement: Accuracy and Precision
111.8K
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.
111.8K
Random and Systematic Errors
15.5K
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...
15.5K
Uncertainty in Measurement: Reading Instruments
54.9K
Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
54.9K
Contaminants and Errors
431
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
Another key consideration is determining the appropriate number of samples required to...
431
Testing a Claim about Standard Deviation
3.0K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
3.0K
Random Error
9.9K
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...
9.9K

