171 citations · 480 across the 25 of their papers we have counts for
8 papers · 1 filter
Unleashing the Potential of LLMs for Quantum Computing: A Study in Quantum Architecture Design
Zhiding Liang, Jinglei Cheng, Rui Yang +6
Large Language Models (LLMs) contribute significantly to the development of conversational AI and has great potentials to assist the scientific research in various areas. This pape…
Exponential speedups for quantum walks in random hierarchical graphs
Shankar Balasubramanian, Tongyang Li, Aram Harrow
There are few known exponential speedups for quantum algorithms and these tend to fall into even fewer families. One speedup that has mostly resisted generalization is the use of q…
Fast and Practical Quantum-Inspired Classical Algorithms for Solving Linear Systems
Qian Zuo, Tongyang Li
We propose fast and practical quantum-inspired classical algorithms for solving linear systems. Specifically, given sampling and query access to a matrix $A\in\mathbb{R}^{m\times n…
Near-Optimal Quantum Coreset Construction Algorithms for Clustering
Yecheng Xue, Xiaoyu Chen, Tongyang Li +1
-Clustering in (e.g., -median and -means) is a fundamental machine learning problem. While near-linear time approximation algorithms were known in the class…
Fidelity estimator, randomized benchmarking and ZNE for quantum pulses
Jinglei Cheng, Zhiding Liang, Rui Yang +4
Most previous research focused on designing pulse programs without considering the performance of individual elements or the final fidelity. To evaluate the performance of quantum…
Logarithmic-Regret Quantum Learning Algorithms for Zero-Sum Games
Minbo Gao, Zhengfeng Ji, Tongyang Li +1
We propose the first online quantum algorithm for solving zero-sum games with regret under the game setting. Moreover, our quantum algorithm computes an $\varepsi…
Towards Advantages of Parameterized Quantum Pulses
Zhiding Liang, Jinglei Cheng, Zhixin Song +7
The advantages of quantum pulses over quantum gates have attracted increasing attention from researchers. Quantum pulses offer benefits such as flexibility, high fidelity, scalabil…
Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret
Han Zhong, Jiachen Hu, Yecheng Xue +2
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 pro…