We construct quantum algorithms to compute the solution and/or physical observables of
nonlinear ordinary differential equations (ODEs) and nonlinear Hamilton-Jacobi equations …

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

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 …

We initiate the study of utilizing Quantum Langevin Dynamics (QLD) to solve optimization
problems, particularly those non-convex objective functions that present substantial …

Learning complex quantum processes is a central challenge in many areas of quantum
computing and quantum machine learning, with applications in quantum benchmarking …

Estimating the volume of a convex body is a central problem in convex geometry and can be
viewed as a continuous version of counting. We present a quantum algorithm that estimates …

We present quantum algorithms for sampling from non-logconcave probability distributions
in the form of $\pi (x)\propto\exp (-\beta f (x)) $. Here, $ f $ can be written as a finite sum $ f …

We propose a novel quantum algorithm for solving linear optimization problems by quantum-
mechanical simulation of the central path. While interior point methods follow the central …

We analyze generalizations of algorithms based on the short-path framework first proposed
by Hastings [Quantum 2, 78 (2018)], which has been extended and shown by Dalzell et …

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 …