no code implementations • 9 Apr 2024 • Wei Zi, Siyi Wang, Hyunji Kim, Xiaoming Sun, Anupam Chattopadhyay, Patrick Rebentrost
In recent years, Quantum Machine Learning (QML) has increasingly captured the interest of researchers.
no code implementations • 16 Jan 2024 • Qixin Zhang, Zongqi Wan, Zengde Deng, Zaiyi Chen, Xiaoming Sun, Jialin Zhang, Yu Yang
The fundamental idea of our boosting technique is to exploit non-oblivious search to derive a novel auxiliary function $F$, whose stationary points are excellent approximations to the global maximum of the original DR-submodular objective $f$.
no code implementations • 21 May 2023 • Zongqi Wan, Jialin Zhang, Wei Chen, Xiaoming Sun, Zhijie Zhang
Then we reduce submodular bandit with partition matroid constraint and bandit sequential monotone maximization to the online bandit learning of the monotone multi-linear DR-submodular functions, attaining $O(T^{2/3}\log T)$ of $(1-1/e)$-regret in both problems, which improve the existing results.
no code implementations • 16 Nov 2022 • He-Liang Huang, Xiao-Yue Xu, Chu Guo, Guojing Tian, Shi-Jie Wei, Xiaoming Sun, Wan-su Bao, Gui-Lu Long
To address this challenge, several near-term quantum computing techniques, including variational quantum algorithms, error mitigation, quantum circuit compilation and benchmarking protocols, have been proposed to characterize and mitigate errors, and to implement algorithms with a certain resistance to noise, so as to enhance the capabilities of near-term quantum devices and explore the boundaries of their ability to realize useful applications.
no code implementations • 30 May 2022 • Zongqi Wan, Zhijie Zhang, Tongyang Li, Jialin Zhang, Xiaoming Sun
In this paper, we study MAB and SLB with quantum reward oracles and propose quantum algorithms for both models with $O(\mbox{poly}(\log T))$ regrets, exponentially improving the dependence in terms of $T$.
no code implementations • 27 Apr 2022 • Zongqi Wan, Xiaoming Sun, Jialin Zhang
Our lower bound works even when the loss sequence is oblivious but the delay is non-oblivious.
no code implementations • 23 Mar 2022 • Zhihuai Chen, Siyao Guo, Qian Li, Chengyu Lin, Xiaoming Sun
We show how to distinguish circuits with $\log k$ negations (a. k. a $k$-monotone functions) from uniformly random functions in $\exp\left(\tilde{O}\left(n^{1/3}k^{2/3}\right)\right)$ time using random samples.
no code implementations • 13 Sep 2021 • Zhijie Zhang, Wei Chen, Xiaoming Sun, Jialin Zhang
We study the online influence maximization (OIM) problem in social networks, where the learner repeatedly chooses seed nodes to generate cascades, observes the cascade feedback, and gradually learns the best seeds that generate the largest cascade in multiple rounds.
no code implementations • 7 Jun 2021 • Zhijie Zhang, Wei Chen, Xiaoming Sun, Jialin Zhang
Our IMS algorithms enhance the learning-and-then-optimization approach by allowing a constant approximation ratio even when the diffusion parameters are hard to learn, and we do not need any assumption related to the network structure or diffusion parameters.
no code implementations • ICML 2020 • Wei Chen, Xiaoming Sun, Jialin Zhang, Zhijie Zhang
We revisit the optimization from samples (OPS) model, which studies the problem of optimizing objective functions directly from the sample data.
no code implementations • 13 Apr 2018 • Riling Li, Bujiao Wu, Mingsheng Ying, Xiaoming Sun, Guangwen Yang
We design a large-scale simulator of universal random quantum circuits, often called 'quantum supremacy circuits', and implement it on Sunway TaihuLight.
Quantum Physics
no code implementations • 23 Nov 2016 • Jia Zhang, Zheng Wang, Qian Li, Jialin Zhang, Yanyan Lan, Qiang Li, Xiaoming Sun
In the guaranteed delivery scenario, ad exposures (which are also called impressions in some works) to users are guaranteed by contracts signed in advance between advertisers and publishers.
no code implementations • 22 Nov 2016 • Jia Zhang, Weidong Ma, Tao Qin, Xiaoming Sun, Tie-Yan Liu
We then extend our mechanism to the general case and achieve a competitive ratio $\frac{1}{42\log k\log T}$ for both social welfare and revenue, where $T$ is the ratio of the maximum request length to the minimum request length and $k$ is the ratio of the maximum request value density to the minimum request value density.