Search Results for author:
Found 3 papers, 0 papers with code
Quantum federated learning through blind quantum computing
In this paper, we introduce a quantum protocol for distributed learning that is able to utilize the computational power of the remote quantum servers while keeping the private data safe.
Tensor networks and efficient descriptions of classical data
We investigate the potential of tensor network based machine learning methods to scale to large image and text data sets.
Quantum Adversarial Machine Learning
Adversarial machine learning is an emerging field that focuses on studying vulnerabilities of machine learning approaches in adversarial settings and developing techniques accordingly to make learning robust to adversarial manipulations.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
