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Yes, you can use Tensorflow to perform this. Below are few mentioned: A cross-platform Python library for quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations Introduction of the first dedicated machine learning platform for quantum computers It's Documentation

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Qiskit uses the datasets provided by sklearn and all the ones built into Qiskit are available here. You could follow how these methods work to load your own dataset if you wanted to. You should be able to use the return from the call to the dataset directly for classical machine learning methods.

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In an article Towards Pricing Financial Derivatives with an IBM Quantum Computer PCA is implemented in a practical way with an example. Operator $U_{prep}$ is realized with $\mathrm{U3}$ gates but parameters for some gates presented in the article seems wrong (maybe typo). See this thread for more information, correct $\mathrm{U3}$ parameters values and a ...

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You can also run QML/QAI on a real Quantum Computer using Qiskit. Here are two sample Jupyter Notebooks for qSVM and qGAN. https://quantum-computing.ibm.com/jupyter/tutorial/advanced/aqua/artificial_intelligence/index.ipynb

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