This approach is based on the idea of knowledge distillation and uses elements of curriculum learning. It works by constructing a set of simple, but rich-in- ...
Mar 19, 2022 · We introduce an efficient training framework for constructing machine learning-based emulators. Our approach is based on the idea of knowledge distillation.
This approach is based on the idea of knowledge distillation and uses elements of curriculum learning. It works by constructing a set of simple, but rich-in- ...
Mar 18, 2022 · Our approach is based on the idea of knowledge distillation and uses elements of curriculum learning. It works by constructing a set of simple, ...
(2023). 10. Emulating Quantum Dynamics with Neural Networks via Knowledge Distillation Yu Yao, Chao Cao, Stephan Haas, and Marcin Abram, Front. Mater. Sec ...
Introduction. We introduce a novel framework for training machine learning-based emulators. It combines ideas of knowledge distillation and curriculum learning.
Emulating quantum dynamics with neural networks via knowledge distillation. Y Yao, C Cao, S Haas, M Agarwal, D Khanna, M Abram. Frontiers in Materials 9 ...
People also ask
What is knowledge distillation in neural network?
Can quantum computers train neural networks?
Emulating Quantum Dynamics with Neural Networks via Knowledge Distillation ... We focus on the question of how the emulator learns the rules of quantum dynamics ...
Emulating Quantum Dynamics with Neural Networks via Knowledge Distillation. #1. Yu Yao(. Southern California U. ) ,. Chao Cao(. Southern California U ...
High-fidelity quantum dynamics emulators can be used to predict the time evolution of complex physical systems. Here, we introduce an efficient training ...