SQAI-NCTS Workshop on Tensor Network and Quantum Embedding

Hybrid Classical-Quantum Architectures for Quantum Machine Learning

26 Mar 2024, 14:00

1h

Room 1220, 2F, Faculty of Science Bldg. 4 (Hongo Campus, The University of Tokyo)

Contributed talk Invited talks

Speaker

Ying-Jer Kao (Center for Theoretical Physics and Department of Physics, National Taiwan University, Taipei 10607, Taiwan)

Description

In this talk, I will introduce a hybrid model combining a quantum-inspired tensor network (TN) and a variational quantum circuit (VQC) to perform supervised and reinforcement learning tasks. This architecture allows for the classical and quantum parts of the model to be trained simultaneously, providing an end-to-end training framework. We show that compared to the principal component analysis, a tensor network based on the matrix product state with low bond dimensions performs better as a feature extractor for the input data of the variational quantum circuit in the binary and ternary classification of MNIST and Fashion-MNIST datasets. We also use this architecture to perform quantum reinforcement learning on the MiniGrid environment with 147-dimensional inputs.The hybrid TN-VQC architecture provides a natural way to perform efficient compression of the input dimension, enabling further quantum machine learning applications on noisy intermediate-scale quantum devices. Finally, I will discuss some regularization methods to address the issue of barren plateaus during training for multi-layer VQC.