Variational quantum reinforcement learning via evolutionary optimization

Samuel Yen-Chi Chen

doi:10.1088/2632-2153/ac4559

2022-02-15

Samuel Yen-Chi Chen *

DOI：10.1088/2632-2153/ac4559

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摘要

En 中文

Recent advances in classical reinforcement learning (RL) and quantum computation point to a promising direction for performing RL on a quantum computer. However, potential applications in quantum RL are limited by the number of qubits available in modern quantum devices. Here, we present two frameworks for deep quantum RL tasks using gradient-free evolutionary optimization. First, we apply the amplitude encoding scheme to the Cart-Pole problem, where we demonstrate the quantum advantage of parameter saving using amplitude encoding. Second, we propose a hybrid framework where the quantum RL agents are equipped with a hybrid tensor network-variational quantum circuit (TN-VQC) architecture to handle inputs of dimensions exceeding the number of qubits. This allows us to perform quantum RL in 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 RL applications on noisy intermediate-scale quantum devices.

Keyword:

quantum machine learning

artificial intelligence

variational quantum circuits

quantum neural networks

reinforcement learning

evolutionary optimization

期刊

Machine Learning-Science and Technology

IF:

4.6

论文数:

1.1K

被引数:

3.4K

机构

united states department of energy (doe)

学者数:

11.3W

论文数: 9.6W

被引数: 246

Brookhaven National Laboratory

学者数:

6.4K

论文数: 4.9K

被引数: 1.9W

引用论文

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IF3.5

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OAAI

Wu, Sau Lan; Chan, Jay; Guan, Wen; Sun, Shaojun; Wang, Alex; Zhou, Chen; Livny, Miron; Carminati, Federico; Di Meglio, Alberto; Li, Andy C. Y.; Lykken, Joseph; Spentzouris, Panagiotis; Chen, Samuel Yen-Chi; Yoo, Shinjae; Wei, Tzu-Chieh

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IF0

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