105 citations · 557 across the 25 of their papers we have counts for
9 papers · 1 filter
Quantum Convolutional Neural Networks for High Energy Physics Data Analysis
Samuel Yen-Chi Chen, Tzu-Chieh Wei, Chao Zhang +2
This work presents a quantum convolutional neural network (QCNN) for the classification of high energy physics events. The proposed model is tested using a simulated dataset from t…
Application of Quantum Machine Learning using the Quantum Variational Classifier Method to High Energy Physics Analysis at the LHC on IBM Quantum Computer Simulator and Hardware with 10 qubits
Sau Lan Wu, Jay Chan, Wen Guan +12
One of the major objectives of the experimental programs at the LHC is the discovery of new physics. This requires the identification of rare signals in immense backgrounds. Using…
Hybrid quantum-classical classifier based on tensor network and variational quantum circuit
Samuel Yen-Chi Chen, Chih-Min Huang, Chia-Wei Hsing +1
One key step in performing quantum machine learning (QML) on noisy intermediate-scale quantum (NISQ) devices is the dimension reduction of the input data prior to their encoding. T…
Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition
Chao-Han Huck Yang, Jun Qi, Samuel Yen-Chi Chen +4
We propose a novel decentralized feature extraction approach in federated learning to address privacy-preservation issues for speech recognition. It is built upon a quantum convolu…
A Unified Framework for Quantum Supervised Learning
Nhat A. Nghiem, Samuel Yen-Chi Chen, Tzu-Chieh Wei
Quantum machine learning is an emerging field that combines machine learning with advances in quantum technologies. Many works have suggested great possibilities of using near-term…
Quantum Long Short-Term Memory
Samuel Yen-Chi Chen, Shinjae Yoo, Yao-Lung L. Fang
Long short-term memory (LSTM) is a kind of recurrent neural networks (RNN) for sequence and temporal dependency data modeling and its effectiveness has been extensively established…
Adversarial Robustness of Deep Convolutional Candlestick Learner
Jun-Hao Chen, Samuel Yen-Chi Chen, Yun-Cheng Tsai +1
Deep learning (DL) has been applied extensively in a wide range of fields. However, it has been shown that DL models are susceptible to a certain kinds of perturbations called \emp…
Data Augmentation for Deep Candlestick Learner
Chia-Ying Tsao, Jun-Hao Chen, Samuel Yen-Chi Chen +1
To successfully build a deep learning model, it will need a large amount of labeled data. However, labeled data are hard to collect in many use cases. To tackle this problem, a bun…
Explainable Deep Convolutional Candlestick Learner
Jun-Hao Chen, Samuel Yen-Chi Chen, Yun-Cheng Tsai +1
Candlesticks are graphical representations of price movements for a given period. The traders can discovery the trend of the asset by looking at the candlestick patterns. Although…