I need an explanation behind this calculation

In summary, when expressing exp(\frac{i\sigma\cdot \widehat{n}\phi}{2}) as a series expansion, we can assume that (\sigma\cdot \widehat{n})^{2}=I because of the properties of Pauli matrices. This is shown through the use of permutation tensors and the fact that n_i n_j \sigma_i \sigma_j = I. This assumption simplifies the series expansion and makes it easier to work with.
  • #1
Demon117
165
1
When expressing [itex]exp(\frac{i\sigma\cdot \widehat{n}\phi}{2})[/itex] as a series expansion, why can we make the assumption that [itex](\sigma\cdot \widehat{n})^{2}=I[/itex]?

Someone I asked on campus showed me this and I don't quite understand its implications (partly because I don't quite understand permutation tensors):

[itex]\sigma_{i}n_{i}\sigma_{j}n_{j}=n_{i}n_{j}(\delta_{ij}I+i\epsilon_{ijk}\sigma_{k})=(\widehat{n}\cdot \widehat{n})I+0=I[/itex]

The middle two steps are really foreign to me, but I get the component parts in front and the last part. Could someone explain in further detail why the middle two parts exist?
 
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  • #2
matumich26 said:
When expressing [itex]exp(\frac{i\sigma\cdot \widehat{n}\phi}{2})[/itex] as a series expansion, why can we make the assumption that [itex](\sigma\cdot \widehat{n})^{2}=I[/itex]?

Someone I asked on campus showed me this and I don't quite understand its implications (partly because I don't quite understand permutation tensors):

[itex]\sigma_{i}n_{i}\sigma_{j}n_{j}=n_{i}n_{j}(\delta_{ij}I+i\epsilon_{ijk}\sigma_{k})=(\widehat{n}\cdot \widehat{n})I+0=I[/itex]

The middle two steps are really foreign to me, but I get the component parts in front and the last part. Could someone explain in further detail why the middle two parts exist?

Pauli matrices have the following IMPORTANT properties that you probably should verify and memorize:
[itex]\sigma_j^2 = I\,,[/itex] [itex][\sigma_i, \sigma_j] = 2 i\epsilon_{ijk} \sigma_k \,,[/itex] [itex]\{\sigma_i, \sigma_j\} = 2\delta_{ij} I[/itex]

Now, from the second step:
[itex]n_i n_j \sigma_i \sigma_j =
\frac12 n_i n_j (\sigma_i \sigma_j + \sigma_j \sigma_i)
= \frac12 n_i n_j \{\sigma_i, \sigma_j\} = n_i n_j \delta_i I = I[/itex]

What the person you used is this:
[itex]\frac12 \left( [\sigma_i, \sigma_j] + \{\sigma_i, \sigma_j\} \right) = \delta_{ij} I + i\epsilon_{ijk} \sigma_k[/itex]
 
Last edited:
  • #3
mathfeel said:
Pauli matrices have the following IMPORTANT properties that you probably should verify and memorize:
[itex]\sigma_j^2 = I\,,[/itex] [itex][\sigma_i, \sigma_j] = 2 i\epsilon_{ijk} \sigma_k \,,[/itex] [itex]\{\sigma_i, \sigma_j\} = 2\delta_{ij} I[/itex]

Now, from the second step:
[itex]n_i n_j \sigma_i \sigma_j =
\frac12 n_i n_j (\sigma_i \sigma_j + \sigma_j \sigma_i)
= \frac12 n_i n_j \{\sigma_i, \sigma_j\} = n_i n_j \delta_i I = I[/itex]

What the person you used is this:
[itex]\frac12 \left( [\sigma_i, \sigma_j] + \{\sigma_i, \sigma_j\} \right) = \delta_{ij} I + i\epsilon_{ijk} \sigma_k[/itex]

Wow, that is actually very helpful. Thank you. But I wonder, is the last portion [itex]\frac12 \left( [\sigma_i, \sigma_j] + \{\sigma_i, \sigma_j\} \right) = \delta_{ij} I + i\epsilon_{ijk} \sigma_k[/itex] necessary? It looks as though you can get by without that from the second step. Thanks again.
 

Related to I need an explanation behind this calculation

1. What is the purpose of this calculation?

The purpose of this calculation is to determine a numerical value or relationship between variables in order to understand a particular phenomenon or problem.

2. How was the calculation performed?

The calculation was performed using a specific set of mathematical operations and equations based on the given data and variables.

3. Can you provide a step-by-step explanation of the calculation?

Yes, I can provide a detailed explanation of each step taken in the calculation, including the equations and reasoning behind each step.

4. What assumptions were made in this calculation?

Assumptions may vary depending on the specific calculation, but some common assumptions include linearity, normality, and independence of variables.

5. How accurate is this calculation?

The accuracy of the calculation depends on the accuracy of the given data and the assumptions made. It is important to carefully evaluate the data and assumptions to determine the level of accuracy of the calculation.

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