8 citations · 29 across the 48 of their papers we have counts for
15 papers · 1 filter
Quantum-Train-Based Distributed Multi-Agent Reinforcement Learning
Kuan-Cheng Chen, Samuel Yen-Chi Chen, Chen-Yu Liu +1
In this paper, we introduce Quantum-Train-Based Distributed Multi-Agent Reinforcement Learning (Dist-QTRL), a novel approach to addressing the scalability challenges of traditional…
Programming Variational Quantum Circuits with Quantum-Train Agent
Chen-Yu Liu, Samuel Yen-Chi Chen, Kuan-Cheng Chen +2
In this study, the Quantum-Train Quantum Fast Weight Programmer (QT-QFWP) framework is proposed, which facilitates the efficient and scalable programming of variational quantum cir…
Quantum-Trained Convolutional Neural Network for Deepfake Audio Detection
Chu-Hsuan Abraham Lin, Chen-Yu Liu, Samuel Yen-Chi Chen +1
The rise of deepfake technologies has posed significant challenges to privacy, security, and information integrity, particularly in audio and multimedia content. This paper introdu…
A Quantum Circuit-Based Compression Perspective for Parameter-Efficient Learning
Chen-Yu Liu, Chao-Han Huck Yang, Hsi-Sheng Goan +1
Quantum-centric supercomputing presents a compelling framework for large-scale hybrid quantum-classical tasks. Although quantum machine learning (QML) offers theoretical benefits i…
CompressedMediQ: Hybrid Quantum Machine Learning Pipeline for High-Dimensional Neuroimaging Data
Kuan-Cheng Chen, Yi-Tien Li, Tai-Yu Li +3
This paper introduces CompressedMediQ, a novel hybrid quantum-classical machine learning pipeline specifically developed to address the computational challenges associated with hig…
Quantum-Train with Tensor Network Mapping Model and Distributed Circuit Ansatz
Chen-Yu Liu, Chu-Hsuan Abraham Lin, Kuan-Cheng Chen
In the Quantum-Train (QT) framework, mapping quantum state measurements to classical neural network weights is a critical challenge that affects the scalability and efficiency of h…
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…
Noise-Aware Distributed Quantum Approximate Optimization Algorithm on Near-term Quantum Hardware
Kuan-Cheng Chen, Xiatian Xu, Felix Burt +3
This paper introduces a noise-aware distributed Quantum Approximate Optimization Algorithm (QAOA) tailored for execution on near-term quantum hardware. Leveraging a distributed fra…
Quantum Local Search for Traveling Salesman Problem with Path-Slicing Strategy
Chen-Yu Liu, Hiromichi Matsuyama, Wei-hao Huang +1
We present novel path-slicing strategies integrated with quantum local search to optimize solutions for the Traveling Salesman Problem (TSP), addressing the limitations of current…
Quantum-Train Long Short-Term Memory: Application on Flood Prediction Problem
Chu-Hsuan Abraham Lin, Chen-Yu Liu, Kuan-Cheng Chen
Flood prediction is a critical challenge in the context of climate change, with significant implications for ecosystem preservation, human safety, and infrastructure protection. In…
QTRL: Toward Practical Quantum Reinforcement Learning via Quantum-Train
Chen-Yu Liu, Chu-Hsuan Abraham Lin, Chao-Han Huck Yang +2
Quantum reinforcement learning utilizes quantum layers to process information within a machine learning model. However, both pure and hybrid quantum reinforcement learning face cha…
Parallel Quantum Local Search via Evolutionary Mechanism
Chen-Yu Liu, Kuan-Cheng Chen
We propose an innovative Parallel Quantum Local Search (PQLS) methodology that leverages the capabilities of small-scale quantum computers to efficiently address complex combinator…
Quantum-Train: Rethinking Hybrid Quantum-Classical Machine Learning in the Model Compression Perspective
Chen-Yu Liu, En-Jui Kuo, Chu-Hsuan Abraham Lin +4
We introduces the Quantum-Train(QT) framework, a novel approach that integrates quantum computing with classical machine learning algorithms to address significant challenges in da…
Multi-GPU-Enabled Hybrid Quantum-Classical Workflow in Quantum-HPC Middleware: Applications in Quantum Simulations
Kuan-Cheng Chen, Xiaoren Li, Xiaotian Xu +2
Achieving high-performance computation on quantum systems presents a formidable challenge that necessitates bridging the capabilities between quantum hardware and classical computi…
Training Classical Neural Networks by Quantum Machine Learning
Chen-Yu Liu, En-Jui Kuo, Chu-Hsuan Abraham Lin +4
In recent years, advanced deep neural networks have required a large number of parameters for training. Therefore, finding a method to reduce the number of parameters has become cr…