C: Modelling errors in a measurement

In summary, the conversation is about simulating the communication of Alice to Bob in a continuous variable quantum key distribution protocol, which involves assigning gaussian distributed random numbers to an array and adding a random error to simulate noise. The conversation also discusses the use of a relative error and the need for a Gaussian distribution instead of a uniform one.
  • #1
six7th
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Homework Statement


I'm currently writing a program that will read in a list of gaussian distributed random numbers as an array and will make a 'measurement', that is they will be assigned to another array with an associated error. This is to try and simulate the communication of Alice to Bob in a continuous variable quantum key distribution protocol.

In such a protocol there will be gaussian noise and therefore Bob will not receive the exact signal that was sent. For example, if there was a 50% spread around Alice's value that Bob could measure, how would I go about doing this?

2. The attempt at a solution

for(i=0;i<=size;i++)
{

double a = (rand() % 100) - 50; //Produces a random number between -50 and 50
bob = alice + (a/10); //Bob makes a measurement with a random error between -0.5 and 0.5

fprintf(gaussBob, "%.4lg %.4lg\n",alice,bob);
}

To me what I've done doesn't seem correct as there is just an addition of a random value between the specified range. Any help is appreciated.
 
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  • #2
50% spread is a relative error? Or something absolute?
bob = alice + (a/10); //Bob makes a measurement with a random error between -0.5 and 0.5
I guess this should be a/100.
This gives a uniform distribution, To get a Gaussian there are many algorithms.
 

FAQ: C: Modelling errors in a measurement

What is a modelling error in a measurement?

A modelling error in a measurement is the difference between the true value of a quantity and the value estimated by a mathematical model. This type of error typically occurs due to simplifications or assumptions made in the model, and can lead to inaccuracies in the measurement.

How do modelling errors affect the accuracy of a measurement?

Modelling errors can significantly impact the accuracy of a measurement. If the model used to estimate the value of a quantity is not an accurate representation of the real-world situation, the resulting measurement will also be inaccurate. This can lead to incorrect conclusions and decisions based on the measurement.

What are some common sources of modelling errors in measurements?

Modelling errors can arise from a variety of sources, such as inadequate knowledge of the system being measured, simplifications or assumptions made in the model, or limitations of the measuring instrument. In some cases, external factors such as environmental conditions or human error can also contribute to modelling errors.

How can scientists minimize modelling errors in their measurements?

To minimize modelling errors, scientists can use more complex or accurate models, gather more data to improve the model, or carefully consider and account for potential sources of error. It is also important to regularly validate and refine the model to ensure it accurately reflects the real-world situation.

Can modelling errors ever be completely eliminated in measurements?

It is unlikely that modelling errors can ever be completely eliminated in measurements. This is because all models involve simplifications and assumptions, and there will always be limitations to our understanding of complex systems. However, by continually improving and refining our models, we can reduce the impact of modelling errors on our measurements.

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