Large language models (LLMs) have demonstrated exceptional performance across a wide
range of tasks. These models, powered by advanced deep learning techniques, have …

We consider the problem of minimizing a continuous function given given access to a
natural quantum generalization of a stochastic gradient oracle. We provide two new …

We present a novel quantum algorithm for estimating Gibbs partition functions in sublinear
time with respect to the logarithm of the size of the state space. This is the first speed-up of …

Given a convex function $ f\colon\mathbb {R}^{d}\to\mathbb {R} $, the problem of sampling
from a distribution $\propto e^{-f (x)} $ is called log-concave sampling. This task has wide …

This paper considers the problem for finding the $(\delta,\epsilon) $-Goldstein stationary
point of Lipschitz continuous objective, which is a rich function class to cover a great number …

We initiate the study of quantum algorithms for optimizing approximately convex functions.
Given a convex set $\mathcal {K}\subseteq\mathbb {R}^{n} $ and a function …

Quantum computing is an emerging technology that has been rapidly advancing in the past
decades. In this paper, we conduct a systematic study of quantum lower bounds on finding …

In this work, we propose a generalization of the current most widely used quantum
computing hardware metric known as the quantum volume. The quantum volume specifies a …

Sampling from Gibbs states--states corresponding to system in thermal equilibrium--has
recently been shown to be a task for which quantum computers are expected to achieve …

We present classical and quantum algorithms for approximating partition functions of
classical Hamiltonians at a given temperature. Our work has two main contributions: first, we …