[New Content Request]: End-to-End Quantum Kernel Estimation with Iris Dataset on Real Hardware

URL to the relevant tutorial

https://quantum.cloud.ibm.com/learning/en/courses/quantum-machine-learning/classical-ml-review

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• out-of-date content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

Describe the fix or the content request.

In Quantum Machine Learning (QML), many learners struggle to transition from theoretical "Hello World" circuits to practical data applications on real hardware. I have successfully implemented a complete end-to-end workflow that:

• Loads the classical Iris dataset using Scikit-Learn.
• Encodes the features into a ZZFeatureMap.
• Calculates a Quantum Kernel Matrix using UnitaryOverlap and the SamplerV2 primitive.
• Executes the job on a real QPU (ibm_fez) using the Batch context manager for optimized queue handling.
• Visualizes the resulting similarity matrix using a Seaborn heatmap.

I would like to contribute this as a practical lab exercise or an "Applied QML" section to help others see the physical results of quantum state overlap on real-world data.

What value does the new content bring?

Currently, many tutorials use local simulators which hide the complexities of ISA (Instruction Set Architecture) circuits and Batching. This content brings value by:

• Demonstrating how to use the instance parameter to connect to specific "Open Plan" hubs.
• Showing the physical reality of noise and diagonal values in real QPU results (as seen in the attached heatmap where the diagonal is ~0.8-0.9 instead of a perfect 1.0 due to gate errors).
• Bridging the gap between the popular Python data science stack (pandas, seaborn, scikit-learn) and qiskit-ibm-runtime.

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