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What could be the steps to implement a distance based classifier (eg Qknn) on our dataset using quantum computing?

I read a paper on "Distance based classifier using Iris dataset". I wanted to implement the same on my machine considering any random dataset taking 2 datapoints and 2 features as considered in the paper. SO do i need to make some changes in the gates or where do i need to put my data values?

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    $\begingroup$ This is a very broad question, maybe you could provide more specific information on the specific classification problem you're interested in (input data type, data encoding method), as well as what you've attempted and where you're struggling with the implementation? $\endgroup$ – forky40 Mar 5 at 19:43
  • $\begingroup$ Thank you for your answer. But I am new to this field. I read a paper on "Distance based classifier using Iris dataset". I wanted to implement the same on my machine considering any random dataset taking 2 datapoints and 2 features as considered in the paper. SO do i need to make some changes in the gates or where do i need to put my data values? $\endgroup$ – user14924 Mar 6 at 8:39
  • $\begingroup$ Do you mean this paper: iopscience.iop.org/article/10.1209/0295-5075/119/60002/meta? If you follow the same methods for standardization/normalization on your 2-dimensional data you should be able to set up the same experiment as they did. You need to change the parameters of the gates so that the amplitudes of each encoded quantum state are given by the two features of the corresponding data point (i.e. equation 3) $\endgroup$ – forky40 Mar 6 at 19:12
  • $\begingroup$ please note that you can edit your question to add details. All details relevant to the question should be contained in the post, not the comments. $\endgroup$ – glS Mar 7 at 11:03

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