# How to use quantum methods to process classical data to speed up model training

**URL:** <https://discuss.pennylane.ai/t/how-to-use-quantum-methods-to-process-classical-data-to-speed-up-model-training/1770>\
**Category:** PennyLane Help\
**Created:** [April 1, 2022, 9:41am UTC](https://discuss.pennylane.ai/t/how-to-use-quantum-methods-to-process-classical-data-to-speed-up-model-training/1770 "2022-04-01T09:41:03Z")\
**Posts on this page:** 1\
**Showing post:** 7

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**Author:** ![gojiita\_ku](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/gojiita_ku/32/566_2.png) [@gojiita\_ku](https://discuss.pennylane.ai/u/gojiita_ku)\
**Post date:** [April 7, 2022, 9:12am UTC](https://discuss.pennylane.ai/t/how-to-use-quantum-methods-to-process-classical-data-to-speed-up-model-training/1770/7 "2022-04-07T09:12:52Z")

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Hi @CatalinaAlbornoz, thank you for your reply. Yes, in most cases, people are making effort on generating more samples to improve model performance. But in my case, the dataset is too large. I have about 100,000 samples with 600 features. It is impractical to train my ML model on this dataset in my local environment. So the requirement of my project is to reduce the number of samples using quantum methods. I notice one interesting example about amplitude encoding in [this page](https://pennylane.ai/qml/glossary/quantum_embedding.html), but I don’t know if it could work for my case. I actually post [a new question](http://discuss.pennylane.ai/t/question-about-the-use-of-amplitude-embedding-mentioned-in-pennylanes-page/1808) about it in the forum. If you are interested in it, you may take a look.😊

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