About
Dr. Qi is now a Research Assistant Professor in the Department of Computer Science, Hong Kong Baptist University. Previously, he received his Ph.D. from the School of Electrical and Computer Engineering at Georgia Institute of Technology, advised by Prof. Chin-Hui Lee and Prof. Xiaoli Ma for the research on Speech and Language Processing. His current study focuses on Quantum Techniques in Machine Learning, which investigates the interplay of trainability, generalization, and expressive power in quantum machine learning models as the team explores paths to practical quantum advantage in Artificial Intelligence.
Research Interests
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Quantum Machine Learning
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Quantum Natural Language Processing
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Speech, Language and Signal Processing
Selected Publications
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Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen, Min-Hsiu Hsieh* "Theoretical Error Performance Analysis for Variational Quantum Circuit Based Functional Regression," Nature Publishing Group, npj Quantum Information, Vol. 9, no. 4, 2023 (Impact Factor: 10.89)
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Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen, Javier Tejedor, "Exploiting Low-Rank Tensor-Train Deep Neural Networks Based on Riemannian Gradient Descent with Illustration of Speech Processing," IEEE/ACM Transactions on Audio, Speech and Language Processing, Vol. 31, pp. 633-642, 2023
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Jun Qi, Jun Du, Sabato Marco Siniscalchi, Xiaoli Ma, Chin-Hui Lee, "Analyzing Upper Bounds on Mean Absolute Errors for Deep Neural Network Based Vector-to-Vector Regression," IEEE Transactions on Signal Processing, Vol 68, pp. 3411-3422, 2020
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Jun Qi, Jun Du, Sabato Marco Siniscalchi, Xiaoli Ma, Chin-Hui Lee, “On Mean Absolute Error for Deep Neural Network-based Vector-to-Vector Regression,” IEEE Signal Processing Letters, Vol. 27, pp. 1485-1489, 2020
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Jun Qi, Jun Du, Sabato Marco Siniscalchi, Chin-Hui Lee, "A Theory on Deep Neural Network-based Vector-to-Vector Regression with an Illustration of Its Expressive Power in Speech Enhancement," IEEE/ACM Transactions on Audio, Speech and Language Processing, Vol 27, no. 12, pp. 1932-1943, 2019
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Jun Qi, Xiao-Lei Zhang, Javier Tejedor, "Optimizing Quantum Federated Learning Based on Federated Quantum Natural Gradient Descent," IEEE Intl. Conf. on Acoustic, Speech, and Signal Processing (ICASSP), 2023
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Jun Qi, Javier Tejedor, "Classical-to-Quantum Transfer Learning for Spoken Command Recognition based on Quantum Neural Networks," IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2022
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Jun Qi, Javier Tejedor, "Exploiting Hybrid Models of Tensor-Train Networks for Spoken Command Recognition," IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2022
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Jun Qi, Hu Hu, Yannan Wang, Chao-Han Huck Yang, Sabato Marco Siniscalchi, Chin-Hui Lee, "Tensor-to-Vector Regression for Multi-Channel Speech Enhancement based on Tensor-Train Network,” IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2020
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Jun Qi, Huck Yang, Javier Tejedor, “Submodular Rank Aggregation for Score-based Permutations for Distributed Automatic Speech Recognition,” IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2020
