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plot_histogram should show better when very spread results #13066
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Hi @1ucian0 If you want that, I think it can be done by adding some conditions and statements to the plot_histogram function. I want to work on this if you think it will work. |
I think we could add a parameter for sparsity in the function, in which the user could mention sparse = true to denote that the data is
This would tell the plot_histogram function that data is sparse, and can then internally analyze what the value of number_to_keep should be. OR We could create a sparse function inside the plot_histogram function. The sparse function could trace the data and detect sparsity by analyzing how many values belong below some threshold and accordingly setting the value of number_to_keep parameter. |
@1ucian0 Can you please give some feedback on this and assign this to me? If there is some IBM policy that only in-house developers are allowed to contribute code to Qiskit, you can let me know. Because it has happened to me before, that I wanted to resolve an issue (which was a feature request), and I commented on it, but no one assigned to it me for many days and then it was suddenly assigned to an in-house developer, only hours after he had asked. |
Hi Shivansh, I wouldn't add a boolean flag that makes an internal analysis. I would use that parameter to parametrized that analysis. For example, There is no policy about in-house assignments. However, you have better chances with good-first-issues: https://qisk.it/good-first-issues |
What should we add?
With bigger systems, results are sometimes very sparse (meaning, a lot of results with very low count). Take the following result (it comes from an execution in qiskit-aer with noise, but irrelevant for this point):
When visualizing, this is the result:
This can be addressed via
number_to_keep
, maybe just with a default around 30?The text was updated successfully, but these errors were encountered: