Speaker
Jian Feng Kong
(Institute of High Performance Computing, A*STAR)
Description
Quantum machine learning leverages on quantum parallelism and entanglement to enhance classical machine learning WHITE algorithms performance. Quantum Convolutional Neural Networks (QCNNs) have shown significant potential in classification tasks by exploiting quantum parallelism and entanglement to process quantum data with short range entanglement structure, and avoids the notorious barren plateau problem by construction due to its logarithmic circuit depth. Here we compare the efficiency of QCNN with that of the hardware-efficient ansatz (HEA) in classifying the ground states of two quantum models. We have also studied the compression capabilities of the ground states of one quantum model.
Primary authors
Dr
Jun Yong Khoo
(Institute of High Performance Computing, A*STAR)
Dr
Chee Kwan Gan
(Institute of High Performance Computing, A*STAR)
Dr
Wenjun Ding
Stefano Carrazza
Dr
Jun Ye
(Institute of High Performance Computing, A*STAR)
Jian Feng Kong
(Institute of High Performance Computing, A*STAR)
