Yecheng Xue — accepted AI-conference papers

2024

Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret

Han Zhong, Jiachen Hu, Yecheng Xue, Tongyang Li, Liwei Wang

ICML 2024poster

While quantum reinforcement learning (RL) has attracted a surge of attention recently, its theoretical understanding is limited. In particular, it remains elusive how to design provably efficient quantum RL algorithms that can address the exploration-exploitation trade-off. To this end, we propose a…

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2023

Near-Optimal Quantum Coreset Construction Algorithms for Clustering

Yecheng Xue, Xiaoyu Chen, Tongyang Li, Shaofeng H.-C. Jiang

ICML 2023poster

$k$-Clustering in $\mathbb{R}^d$ (e.g., $k$-median and $k$-means) is a fundamental machine learning problem. While near-linear time approximation algorithms were known in the classical setting for a dataset with cardinality $n$, it remains open to find sublinear-time quantum algorithms. We give quan…

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