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XanaduAI / QHack2021

Official repo for QHack—the quantum machine learning hackathon
https://qhack.ai
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[ENTRY] masK IT #63

Open cDenius opened 3 years ago

cDenius commented 3 years ago

Team Name:

cirKITers

Project Description:

Nowadays training parameterized quantum circuits is very popular to explore the space of quantum states. This field heavily borrows from existing machine learning approaches. Similar to the effect of vanishing gradients in machine learning, we explore different plateaus such as local minima as well as Barren Plateaus in gradients making the training very hard or even unfeasible for various use cases. Inspired by the classical dropouts in machine learning we explore the impacts of (temporarily) removing randomly selected gates from the circuit. Our experiments show that ensemble-based dropouts can speed up training or even enable training in presence of plateaus for parameterized quantum circuits.

Presentation:

Presentation

Source code:

Code

co9olguy commented 3 years ago

Thanks for the submission! We hope you have enjoyed participating in QHack :smiley:

We will be assessing the entries and contacting the winners separately. Winners will be publicly announced sometime in the next month.

We will also be freezing the GitHub repo as we sort through the submitted projects, so you will not be able to update this submission.