New content to explain the dimension change for the kernel matrix for train and test as 90x90, 30x90

[sudikiniirs]

URL to the relevant course

https://quantum.cloud.ibm.com/learning/en/courses/quantum-machine-learning/quantum-kernel-methods#steps-2-and-3-optimize-problem-and-execute-using-primitives

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• [x] new content request
• [ ] typo
• [ ] code bug
• [ ] out-of-date content
• [ ] broken link
• [ ] other

Describe the fix or the content request.

=================== I request a new content ================

Kernel matrix used for training is consisting of the training data of size 90 which gives a 90x90 kernel but the test kernel has a size of 30x90 where the kernel of the (test data, train data) is found out. A considerable explanation on the same and how svc handles it will be highly beneficial for a learner to understand the concept. I also request to show the kernel graphs for easier interpretation.

Fig. Kernel matrix for the train data Fig. Kernel matrix for the test data

Thank you for reading such a long message.

Best regards, Sudikin Pramanik PhD scholar Quantum Machine Learning IIT Ropar India

For new content requests - if the request is accepted, do you want to write the content?

I will write (or already have written) a draft of the proposed content