Quantum computing has undergone rapid development over recent years: from first conceptualization in the 1980s, and early proof of principles for hardware in the 2000s, quantum computers can now be built with hundreds of qubits. While the technology remains in its infancy, the fast progress of quantum hardware has led many to assert that so-called Noisy-Intermediate Scale Quantum (NISQ) devices could outperform conventional computers shortly. Particularly, the Variational Quantum Eigensolver (VQE) was put forth to be the most promising algorithm on NISQ devices because VQE admits only a small number of qubits and shows some degree of noise resilience. The VQE mechanisms are often cast as hybrid algorithms that practically allow a Variational Quantum Circuit (VQC) with classical machine learning models. In this report, for one thing, we will characterize the VQC-based quantum neural networks (QNN) on NISQ devices in terms of theoretical and empirical study; for another, we aim at investigating new applications of VQC-based QNN for automatic speech recognition and natural language processing.
Dr. Jun Qi is currently a Tenure-Track Assistant Professor in the Department of Electronic Engineering at Fudan University, Shanghai, China. Dr. Jun Qi received his Ph.D. degree from the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA in May 2022. Dr. Qi’s research focuses on quantum machine learning theory, quantum optimization algorithms, and quantum speech and NLP applications. Dr. Qi authored and co-authored many published papers and book chapters in the fields of Quantum Technologies, Speech Recognition, and Signal Processing. Moreover, Dr. Qi was the recipient of 1st prize in Xanadu AI Quantum Machine Learning Competition in 2019, and his ICASSP paper about quantum speech recognition was nominated as the best paper candidate in 2022. Dr. Qi gave tutorials on Quantum Machine Learning for Speech and Language Processing at the venues of IJCAI’21, ICASSP’22, and ISCSLP’22.
IN THIS SESSION
You will learn:
- VQC-based QNN on NISQ devices
- Noise resilience of VQC
- VQC-based QNN for automatic speech recognition and natural language processing.
TIME & AGENDA: (Date/time and activities involved)
Oct 22, 2022 (Saturday)
9 PM (U.S. East Coast)
6 PM (U.S. West Coast)
Oct 23, 2022 (Sunday)
9 AM (Beijing Time)
This is a one-hour event including a 45 min presentation followed by a 15 min discussion. The presentation will be held in Mandarin.
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