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I am trying to perform a classification task with qiskit's VQC. The dataset I am using has a large number of dimensions/features/columns. I am trying to figure out which feature map works best. Also, I only found four feature maps in qiskit documentation, three under Data Encoding Circuits and one here, are there more available ?

  • Without dimentionality reduction is there any other way of encoding more features on fewer qubits (like the RawFeatureVector) ?

  • How to theoretically select the best feature map ?

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Maybe this article can help you: 'Automatic design of quantum feature maps', Sergio Altares-López, Angela Ribeiro and Juan José García-Ripoll, 19 August 2021 - https://doi.org/10.1088/2058-9565/ac1ab1.

They describe a technique to generate optimal quantum feature maps by using multiobjetive genetic algorithms. While the first objective is to increase the accuracy in the predictions on unseen data, building robust classifiers with generalisation power; the second objective of this technique is to reduce the complexity of the quantum circuits.

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    $\begingroup$ While this link may answer the question, it is better to include the essential parts of the answer here and provide the link for reference. Link-only answers can become invalid if the linked page changes. - From Review $\endgroup$
    – Martin Vesely
    Jun 15, 2022 at 6:20

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