5 citations · 9 across the 11 of their papers we have counts for
11 papers
Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuits
Yu-Ting Lee, Samuel Yen-Chi Chen, Fu-Chieh Chang
While parameterized quantum computations have shown success in standard reinforcement learning (RL), whether these advantages adapt to hierarchical RL (HRL) remains a critical open…
MADQRL: Distributed Quantum Reinforcement Learning Framework for Multi-Agent Environments
Abhishek Sawaika, Samuel Yen-Chi Chen, Udaya Parampalli +1
Reinforcement learning (RL) is one of the most practical ways to learn from real-life use-cases. Motivated from the cognitive methods used by humans makes it a widely acceptable st…
Learning to Program Quantum Measurements for Machine Learning
Samuel Yen-Chi Chen, Huan-Hsin Tseng, Hsin-Yi Lin +1
The rapid advancements in quantum computing (QC) and machine learning (ML) have sparked significant interest, driving extensive exploration of quantum machine learning (QML) algori…
Learning to Measure Quantum Neural Networks
Samuel Yen-Chi Chen, Huan-Hsin Tseng, Hsin-Yi Lin +1
The rapid progress in quantum computing (QC) and machine learning (ML) has attracted growing attention, prompting extensive research into quantum machine learning (QML) algorithms…
Evolutionary Optimization for Designing Variational Quantum Circuits with High Model Capacity
Samuel Yen-Chi Chen
Recent advancements in quantum computing (QC) and machine learning (ML) have garnered significant attention, leading to substantial efforts toward the development of quantum machin…
Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits
Jun Qi, Chao-Han Yang, Samuel Yen-Chi Chen +3
Quantum Machine Learning (QML) offers tremendous potential but is currently limited by the availability of qubits. We introduce an innovative approach that utilizes pre-trained neu…
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…
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…
Differentiable Quantum Architecture Search in Asynchronous Quantum Reinforcement Learning
Samuel Yen-Chi Chen
The emergence of quantum reinforcement learning (QRL) is propelled by advancements in quantum computing (QC) and machine learning (ML), particularly through quantum neural networks…
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…
Efficient quantum recurrent reinforcement learning via quantum reservoir computing
Samuel Yen-Chi Chen
Quantum reinforcement learning (QRL) has emerged as a framework to solve sequential decision-making tasks, showcasing empirical quantum advantages. A notable development is through…