Zobrazeno 1 - 10
of 301
pro vyhledávání: '"Li, Tongyang"'
Finite-sum optimization has wide applications in machine learning, covering important problems such as support vector machines, regression, etc. In this paper, we initiate the study of solving finite-sum optimization problems by quantum computing. Sp
Externí odkaz:
http://arxiv.org/abs/2406.03006
The optimal control problem for open quantum systems can be formulated as a time-dependent Lindbladian that is parameterized by a number of time-dependent control variables. Given an observable and an initial state, the goal is to tune the control va
Externí odkaz:
http://arxiv.org/abs/2405.19245
We systematically investigate quantum algorithms and lower bounds for mean estimation given query access to non-identically distributed samples. On the one hand, we give quantum mean estimators with quadratic quantum speed-up given samples from diffe
Externí odkaz:
http://arxiv.org/abs/2405.12838
Autor:
Zhang, Chenyi, Li, Tongyang
When optimizing machine learning models, there are various scenarios where gradient computations are challenging or even infeasible. Furthermore, in reinforcement learning (RL), preference-based RL that only compares between options has wide applicat
Externí odkaz:
http://arxiv.org/abs/2405.11454
The problem of minimizing the maximum of $N$ convex, Lipschitz functions plays significant roles in optimization and machine learning. It has a series of results, with the most recent one requiring $O(N\epsilon^{-2/3} + \epsilon^{-8/3})$ queries to a
Externí odkaz:
http://arxiv.org/abs/2402.12745
Publikováno v:
Quantum 8, 1409 (2024)
Digital quantum simulation has broad applications in approximating unitary evolution of Hamiltonians. In practice, many simulation tasks for quantum systems focus on quantum states in the low-energy subspace instead of the entire Hilbert space. In th
Externí odkaz:
http://arxiv.org/abs/2312.08867
We initiate the study of utilizing Quantum Langevin Dynamics (QLD) to solve optimization problems, particularly those non-convex objective functions that present substantial obstacles for traditional gradient descent algorithms. Specifically, we exam
Externí odkaz:
http://arxiv.org/abs/2311.15587
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 quantum walks to traverse the welded-tree graph, due to Childs, Cleve,
Externí odkaz:
http://arxiv.org/abs/2307.15062
Autor:
Liang, Zhiding, Cheng, Jinglei, Yang, Rui, Ren, Hang, Song, Zhixin, Wu, Di, Qian, Xuehai, Li, Tongyang, Shi, Yiyu
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 paper attempts to address the following questions: What opportunities do t
Externí odkaz:
http://arxiv.org/abs/2307.08191
Autor:
Zuo, Qian, Li, Tongyang
We propose fast and practical quantum-inspired classical algorithms for solving linear systems. Specifically, given sampling and query access to a matrix $A\in\mathbb{R}^{m\times n}$ and a vector $b\in\mathbb{R}^m$, we propose classical algorithms th
Externí odkaz:
http://arxiv.org/abs/2307.06627