Experimental quantum natural gradient optimization in photonics

no code implementations • 11 Oct 2023 • Yizhi Wang, Shichuan Xue, Yaxuan Wang, Jiangfang Ding, Weixu Shi, Dongyang Wang, Yong liu, Yingwen Liu, Xiang Fu, Guangyao Huang, Anqi Huang, Mingtang Deng, Junjie Wu

Our work opens up a vista of utilizing QNG in photonics to implement practical near-term quantum applications.

Quantum generative adversarial learning in photonics

no code implementations • 1 Oct 2023 • Yizhi Wang, Shichuan Xue, Yaxuan Wang, Yong liu, Jiangfang Ding, Weixu Shi, Dongyang Wang, Yingwen Liu, Xiang Fu, Guangyao Huang, Anqi Huang, Mingtang Deng, Junjie Wu

Quantum Generative Adversarial Networks (QGANs), an intersection of quantum computing and machine learning, have attracted widespread attention due to their potential advantages over classical analogs.

Evaluating the Perceived Safety of Urban City via Maximum Entropy Deep Inverse Reinforcement Learning

no code implementations • 19 Nov 2022 • Yaxuan Wang, Zhixin Zeng, Qijun Zhao

Inspired by expert evaluation policy for urban perception, we proposed a novel inverse reinforcement learning (IRL) based framework for predicting urban safety and recovering the corresponding reward function.

reinforcement-learning Reinforcement Learning (RL)