17 citations · 46 across the 15 of their papers we have counts for
15 papers
An Introduction to Quantum Reinforcement Learning (QRL)
Samuel Yen-Chi Chen
Recent advancements in quantum computing (QC) and machine learning (ML) have sparked considerable interest in the integration of these two cutting-edge fields. Among the various ML…
Federated Quantum-Train with Batched Parameter Generation
Chen-Yu Liu, Samuel Yen-Chi Chen
In this work, we introduce the Federated Quantum-Train (QT) framework, which integrates the QT model into federated learning to leverage quantum computing for distributed learning…
Quantum Gradient Class Activation Map for Model Interpretability
Hsin-Yi Lin, Huan-Hsin Tseng, Samuel Yen-Chi Chen +1
Quantum machine learning (QML) has recently made significant advancements in various topics. Despite the successes, the safety and interpretability of QML applications have not bee…
Quantum Machine Learning Architecture Search via Deep Reinforcement Learning
Xin Dai, Tzu-Chieh Wei, Shinjae Yoo +1
The rapid advancement of quantum computing (QC) and machine learning (ML) has given rise to the burgeoning field of quantum machine learning (QML), aiming to capitalize on the stre…
Learning to Program Variational Quantum Circuits with Fast Weights
Samuel Yen-Chi Chen
Quantum Machine Learning (QML) has surfaced as a pioneering framework addressing sequential control tasks and time-series modeling. It has demonstrated empirical quantum advantages…
A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity
Ryan L'Abbate, Anthony D'Onofrio, Samuel Stein +5
Recent advancements have highlighted the limitations of current quantum systems, particularly the restricted number of qubits available on near-term quantum devices. This constrain…