SS-L4.1
Exploiting A Quantum Multiple Kernel Learning Approach for Low-Resource Spoken Command Recognition
Xianyan Fu, Xiao-Lei Zhang, Northwestern Polytechnical University, China; Chao-Han Huck Yang, Georgia Institute of Technology, United States of America; Jun Qi, Hong Kong Baptist University, Hong Kong
Session:
SS-L4: Quantum Machine Learning Algorithms and Applications on NISQ Devices Lecture
Track:
Special Sessions
Location:
Room 209A
Presentation Time:
Wed, 17 Apr, 08:20 - 08:40 (UTC +9)
Session Co-Chairs:
Shinjae Yoo, Brookhaven National Laboratory and Jun Qi, Hong Kong Baptist University
Session SS-L4
SS-L4.1: Exploiting A Quantum Multiple Kernel Learning Approach for Low-Resource Spoken Command Recognition
Xianyan Fu, Xiao-Lei Zhang, Northwestern Polytechnical University, China; Chao-Han Huck Yang, Georgia Institute of Technology, United States of America; Jun Qi, Hong Kong Baptist University, Hong Kong
SS-L4.2: EFFICIENT QUANTUM RECURRENT REINFORCEMENT LEARNING VIA QUANTUM RESERVOIR COMPUTING
Samuel Yen-Chi Chen, Wells Fargo, United States of America
SS-L4.3: QUAPPROX: A FRAMEWORK FOR BENCHMARKING THE APPROXIMABILITY OF VARIATIONAL QUANTUM CIRCUIT
Jinyang Li, George Mason University, United States of America; Ang Li, Pacific Northwest National Laboratory, United States of America; Weiwen Jiang, George Mason University, United States of America
SS-L4.4: QUANTUM FEDERATED LEARNING WITH QUANTUM NETWORKS
Tyler Wang, Stony Brook University, United States of America; Huan-Hsin Tseng, Shinjae Yoo, Brookhaven National Laboratory, United States of America
SS-L4.5: AN EFFICIENT HIERARCHICAL BLOCK COORDINATE DESCENT METHOD FOR TIME-VARYING GRAPHICAL LASSO
Zhaoye Pan, Shanghai University of Finance and Economics, China; Xiaolu Wang, The Hong Kong University of Science and Technology, Hong Kong; Huikang Liu, Shanghai University of Finance and Economics, China; Jun Zhang, The Hong Kong University of Science and Technology, Hong Kong
Contacts