Added complexities

[IAteNoodles]

Describe your change:

Added the various complexities for the quick sort algorithm

• Add an algorithm?
• Fix a bug or typo in an existing algorithm?
• Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
• Documentation change?

Checklist:

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized.
• I know that pull requests will not be merged if they fail the automated tests.
• This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
• All new Python files are placed inside an existing directory.
• All filenames are in all lowercase characters with no spaces or dashes.
• All functions and variable names follow Python naming conventions.
• All function parameters and return values are annotated with Python type hints.
• All functions have doctests that pass the automated testing.
• All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
• If this pull request resolves one or more open issues then the description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".

[tianyizheng02]

I'm not sure if this space complexity analysis is accurate for this specific implementation. From Wikipedia, it seems like the $O(\log n)$ space complexity may only hold for a quick sort implementation that performs partitioning in place (though I may be wrong here). This Python implementation doesn't do in-place partitioning.

[IAteNoodles]

Yes, the best case is for in place it seems. I rechecked.
Since in place is the best way, should I implement with in-place method?

[IAteNoodles]

#11752 In here I have changed the implementation