Search Results for author:
Found 2 papers, 0 papers with code
Variational Denoising for Variational Quantum Eigensolver
We propose variational denoising, an unsupervised learning method that employs a parameterized quantum neural network to improve the solution of VQE by learning from noisy VQE outputs.
NeuRecover: Regression-Controlled Repair of Deep Neural Networks with Training History
Search-based repair techniques for DNNs have potentials to tackle this challenge by enabling localized updates only on "responsible parameters" inside the DNN.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
