Hi @gojiita_ku, I think the idea of reducing the number of samples is interesting. Something you could do is use classical pre-processing to eliminate duplicated data. You could also remove samples that are very similar according to some criteria you define. However, this is likely to affect the quality of your result.
In fact, most classical algorithms improve their performance as you increase the number of samples, so the efforts are more on how to generate more samples, not reduce them. Note that samples are fundamentally different pieces of data that come from a same model or source. In theory there should be no connection between your data points, other than the underlying model you’re trying to uncover or represent with your circuit, so it’s hard to decide how to combine your data. I personally have never seen a quantum application in this sense.
Please let me know if this makes sense for you!