no code implementations • 27 Feb 2024 • Samuel Yen-Chi Chen
This paper introduces the Quantum Fast Weight Programmers (QFWP) as a solution to the temporal or sequential learning challenge.
no code implementations • 23 Feb 2024 • Ryan L'Abbate, Anthony D'Onofrio Jr., Samuel Stein, Samuel Yen-Chi Chen, Ang Li, Pin-Yu Chen, Juntao Chen, Ying Mao
In this study, we concentrate on quantum deep learning and introduce a collaborative classical-quantum architecture called co-TenQu.
no code implementations • 15 Jan 2024 • William Watkins, Heehwan Wang, Sangyoon Bae, Huan-Hsin Tseng, Jiook Cha, Samuel Yen-Chi Chen, Shinjae Yoo
The utility of machine learning has rapidly expanded in the last two decades and presents an ethical challenge.
no code implementations • 21 Dec 2023 • Mahdi Chehimi, Samuel Yen-Chi Chen, Walid Saad, Shinjae Yoo
The proposed federated QLSTM (FedQLSTM) framework is exploited for performing the task of function approximation.
no code implementations • 10 Oct 2023 • Rod Rofougaran, Shinjae Yoo, Huan-Hsin Tseng, Samuel Yen-Chi Chen
The preservation of privacy is a critical concern in the implementation of artificial intelligence on sensitive training data.
no code implementations • 13 Sep 2023 • Samuel Yen-Chi Chen
Quantum reinforcement learning (QRL) has emerged as a framework to solve sequential decision-making tasks, showcasing empirical quantum advantages.
1 code implementation • 17 Jul 2023 • Wei Chen, Yihui Ren, Ai Kagawa, Matthew R. Carbone, Samuel Yen-Chi Chen, Xiaohui Qu, Shinjae Yoo, Austin Clyde, Arvind Ramanathan, Rick L. Stevens, Hubertus J. J. van Dam, Deyu Lu
With this dataset, we trained graph neural fingerprint docking models for high-throughput virtual COVID-19 drug screening.
no code implementations • 19 Apr 2023 • Samuel Yen-Chi Chen
This paper introduces the QDQN-DPER framework to enhance the efficiency of quantum reinforcement learning (QRL) in solving sequential decision tasks.
no code implementations • 12 Jan 2023 • Samuel Yen-Chi Chen
In this paper, we approach this challenge through asynchronous training QRL agents.
no code implementations • 22 Dec 2022 • Elisha Siddiqui Matekole, Esther Ye, Ramya Iyer, Samuel Yen-Chi Chen
A significant amount of theoretical studies have provided various types of QEC codes; one of the notable topological codes is the surface code, and its features, such as the requirement of only nearest-neighboring two-qubit control gates and a large error threshold, make it a leading candidate for scalable quantum computation.
no code implementations • 4 Nov 2022 • Samuel Yen-Chi Chen, Daniel Fry, Amol Deshmukh, Vladimir Rastunkov, Charlee Stefanski
In this work, we approach sequential modeling by applying a reservoir computing (RC) framework to quantum recurrent neural networks (QRNN-RC) that are based on classical RNN, LSTM and GRU.
no code implementations • 26 Oct 2022 • Samuel Yen-Chi Chen
Reinforcement learning (RL) is one of the ML paradigms which can be used to solve complex sequential decision making problems.
no code implementations • 17 Feb 2022 • Chao-Han Huck Yang, Jun Qi, Samuel Yen-Chi Chen, Yu Tsao, Pin-Yu Chen
Our experiments on intent classification show that our proposed BERT-QTC model attains competitive experimental results in the Snips and ATIS spoken language datasets.
no code implementations • 3 Feb 2022 • Yun-Cheng Tsai, Fu-Min Szu, Jun-Hao Chen, Samuel Yen-Chi Chen
Hence, we need to ask about the AI "black box", including why did AI decide to do this or not?
no code implementations • 28 Dec 2021 • Jun-Hao Chen, Yi-Jen Wang, Yun-Cheng Tsai, Samuel Yen-Chi Chen
We apply two representative deep learning privacy-privacy frameworks proposed by Google to financial trading data.
no code implementations • 10 Dec 2021 • Esther Ye, Samuel Yen-Chi Chen
To aid this endeavor, this paper proposes a machine learning-based method to construct quantum circuit architectures.
2 code implementations • 13 Oct 2021 • Riccardo Di Sipio, Jia-Hong Huang, Samuel Yen-Chi Chen, Stefano Mangini, Marcel Worring
In this paper, we discuss the initial attempts at boosting understanding human language based on deep-learning models with quantum computing.
no code implementations • 1 Sep 2021 • Samuel Yen-Chi Chen, Chih-Min Huang, Chia-Wei Hsing, Hsi-Sheng Goan, Ying-Jer Kao
Recent advance in classical reinforcement learning (RL) and quantum computation (QC) points to a promising direction of performing RL on a quantum computer.
1 code implementation • 15 Apr 2021 • En-Jui Kuo, Yao-Lung L. Fang, Samuel Yen-Chi Chen
Recent advances in quantum computing have drawn considerable attention to building realistic application for and using quantum computers.
no code implementations • 22 Mar 2021 • Samuel Yen-Chi Chen, Shinjae Yoo
Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located.
no code implementations • 10 Mar 2021 • William M Watkins, Samuel Yen-Chi Chen, Shinjae Yoo
In this study, we develop a hybrid quantum-classical model that is trained to preserve privacy using differentially private optimization algorithm.
no code implementations • 4 Feb 2021 • Samuel Yen-Chi Chen, Chih-Min Huang, Chia-Wei Hsing, Ying-Jer Kao
We introduce a hybrid model combining a quantum-inspired tensor network and a variational quantum circuit to perform supervised learning tasks.
no code implementations • 15 Jan 2021 • Samuel Yen-Chi Chen, Tzu-Chieh Wei, Chao Zhang, Haiwang Yu, Shinjae Yoo
This research provides a hybrid quantum-classical graph convolutional network (QGCNN) for learning HEP data.
no code implementations • 22 Dec 2020 • Samuel Yen-Chi Chen, Tzu-Chieh Wei, Chao Zhang, Haiwang Yu, Shinjae Yoo
This work presents a quantum convolutional neural network (QCNN) for the classification of high energy physics events.
no code implementations • 21 Dec 2020 • Sau Lan Wu, Jay Chan, Wen Guan, Shaojun Sun, Alex Wang, Chen Zhou, Miron Livny, Federico Carminati, Alberto Di Meglio, Andy C. Y. Li, Joseph Lykken, Panagiotis Spentzouris, Samuel Yen-Chi Chen, Shinjae Yoo, Tzu-Chieh Wei
On the quantum hardware, the quantum variational classifier method has shown promising discrimination power, comparable to that on the quantum simulator.
Quantum Physics High Energy Physics - Experiment
no code implementations • 30 Nov 2020 • Samuel Yen-Chi Chen, Chih-Min Huang, Chia-Wei Hsing, Ying-Jer Kao
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.
2 code implementations • 26 Oct 2020 • Chao-Han Huck Yang, Jun Qi, Samuel Yen-Chi Chen, Pin-Yu Chen, Sabato Marco Siniscalchi, Xiaoli Ma, Chin-Hui Lee
Testing on the Google Speech Commands Dataset, the proposed QCNN encoder attains a competitive accuracy of 95. 12% in a decentralized model, which is better than the previous architectures using centralized RNN models with convolutional features.
Ranked #1 on Keyword Spotting on Google Speech Commands (10-keyword Speech Commands dataset metric)
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 3 Sep 2020 • 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.
2 code implementations • 29 May 2020 • Jun-Hao Chen, Samuel Yen-Chi Chen, Yun-Cheng Tsai, Chih-Shiang Shur
Deep learning (DL) has been applied extensively in a wide range of fields.
2 code implementations • 14 May 2020 • Chia-Ying Tsao, Jun-Hao Chen, Samuel Yen-Chi Chen, Yun-Cheng Tsai
To successfully build a deep learning model, it will need a large amount of labeled data.
2 code implementations • 8 Jan 2020 • Jun-Hao Chen, Samuel Yen-Chi Chen, Yun-Cheng Tsai, Chih-Shiang Shur
Candlesticks are graphical representations of price movements for a given period.
1 code implementation • 30 Jun 2019 • Samuel Yen-Chi Chen, Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen, Xiaoli Ma, Hsi-Sheng Goan
To the best of our knowledge, this work is the first proof-of-principle demonstration of variational quantum circuits to approximate the deep $Q$-value function for decision-making and policy-selection reinforcement learning with experience replay and target network.