6 papers
Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuits
Yu-Ting Lee, Samuel Yen-Chi Chen, Fu-Chieh Chang
Reinforcement learning is one of the most challenging learning paradigms where efficacy and efficiency gains are extremely valuable. Hierarchical reinforcement learning is a varian…
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…