303 citations · 483 across the 20 of their papers we have counts for
21 papers
Optimizing Quantum Federated Learning Based on Federated Quantum Natural Gradient Descent
Jun Qi, Xiao-Lei Zhang, Javier Tejedor
Quantum federated learning (QFL) is a quantum extension of the classical federated learning model across multiple local quantum devices. An efficient optimization algorithm is alwa…
An Ensemble Teacher-Student Learning Approach with Poisson Sub-sampling to Differential Privacy Preserving Speech Recognition
Chao-Han Huck Yang, Jun Qi, Sabato Marco Siniscalchi +1
We propose an ensemble learning framework with Poisson sub-sampling to effectively train a collection of teacher models to issue some differential privacy (DP) guarantee for traini…
Federated Quantum Natural Gradient Descent for Quantum Federated Learning
Jun Qi
The heart of Quantum Federated Learning (QFL) is associated with a distributed learning architecture across several local quantum devices and a more efficient training algorithm fo…
Theoretical Error Performance Analysis for Variational Quantum Circuit Based Functional Regression
Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen +1
The noisy intermediate-scale quantum (NISQ) devices enable the implementation of the variational quantum circuit (VQC) for quantum neural networks (QNN). Although the VQC-based QNN…
Exploiting Low-Rank Tensor-Train Deep Neural Networks Based on Riemannian Gradient Descent With Illustrations of Speech Processing
Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen +1
This work focuses on designing low complexity hybrid tensor networks by considering trade-offs between the model complexity and practical performance. Firstly, we exploit a low-ran…
When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing
Chao-Han Huck Yang, Jun Qi, Samuel Yen-Chi Chen +2
The rapid development of quantum computing has demonstrated many unique characteristics of quantum advantages, such as richer feature representation and more secured protection on…