Colloquium
Department of Physics, NCU
Hybrid Classical-Quantum Architectures for Quantum Machine Learning
Prof. Ying-Jer Kao (高英哲)
Department of physics, NTU
Date 2022.06.07 (Tue)
Place S4-625
Time 14:00-16:00
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Abstract :
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. The architecture is highly adaptable and the classical-quantum boundary can be adjusted according to the availability of the quantum resource by exploiting the correspondence between tensor networks and quantum circuits.