A ResNet-50 + 10-qubit variational circuit raises macro F1 by up to 3.7 points over a classical baseline on blood cell images, but statistical and baseline-control weaknesses prevent a causal claim of quantum advantage.
Fedqnn: Federated learning using quantum neural networks
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2representative citing papers
A federated QLSTM model achieves near-classical accuracy on SUSY classification with under 300 parameters and 20K data points, claiming 100x efficiency gains over baselines.
citing papers explorer
-
Enhancing Blood Cells Classification using Hybrid Quantum Neural Networks
A ResNet-50 + 10-qubit variational circuit raises macro F1 by up to 3.7 points over a classical baseline on blood cell images, but statistical and baseline-control weaknesses prevent a causal claim of quantum advantage.
-
Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics
A federated QLSTM model achieves near-classical accuracy on SUSY classification with under 300 parameters and 20K data points, claiming 100x efficiency gains over baselines.