Qubo tutorial

[alejomonbar]

Before submitting

Please complete the following checklist when submitting a PR:

• [x] Ensure that your tutorial executes correctly, and conforms to the guidelines specified in the README.

• [x] Add a thumbnail link to your tutorial in beginner.rst, or if a QML implementation, in implementations.rst.

• [x] All QML tutorials conform to PEP8 standards. To auto format files, simply pip install black, and then run black -l 100 path/to/file.py.

When all the above are checked, delete everything above the dashed line and fill in the pull request template.

---

Title: The Quadratic Unconstrained Binary Optimization (QUBO) Summary: A tutorial to learn how to represent combinatorial optimization problems using the QUBO formulation. The method is used to represent the Knapsack problem as a QUBO and it is solved using the quantum approximate optimization algorithm.

Relevant references:

Possible Drawbacks:

Related GitHub Issues:

[github-actions[bot]]

Thank you for opening this pull request.

You can find the built site at this link.

Deployment Info:

• Pull Request ID: 733
• Deployment SHA: ec1d4893fcef7228fae8fb78daafa3416f501727 (The Deployment SHA refers to the latest commit hash the docs were built from)

Note: It may take several minutes for updates to this pull request to be reflected on the deployed site.

[github-actions[bot]]

Thank you for opening this pull request.

You can find the built site at this link.

Deployment Info:

• Pull Request ID: 733
• Deployment SHA: 194f108e8e521214f01dd3b41de0819111e4e3e4 (The Deployment SHA refers to the latest commit hash the docs were built from)

Note: It may take several minutes for updates to this pull request to be reflected on the deployed site.

[alejomonbar]

@alvaro-at-xanadu Thank you so much for your comments, I finished with the revision. Please take a look.

[alejomonbar]

Thank you so much for the comments @ludmilaasb I tried my best to address all of them, please let me know what you think!