Max-cut and Hierarchical Learning #78
Open
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
ajrazander
requested review from
aasfaw,
dongreenberg and
woodsp-ibm
as code owners
|
|
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
2 participants
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Summary
The Max-cut_vs_k_means.ipynb notebook implements k-means and the max-cut problem solved by QAOA to cluster data. The max-cut problem solved by QAOA is effectively a binary classifier. The Max-cut_2+_divisive_clustering.ipynb notebook is a sequel tutorial where 2+ clustering groups are formed by applying the same max-cut problem solved by QAOA approach but in a divisive hierarchical scheme.
Details and comments
These are the first tutorials I have contributed, and first time I have done a pull request. I would appreciate feedback so they become as useful as possible. I did see the max-cut tutorial under optimization. However, this has an AI focus--particularly in the second notebook. It also expands to hierarchical analysis which is not covered in any notebook yet.