We propose fast and practical quantum-inspired classical algorithms for
...
k-Clustering in ℝ^d (e.g., k-median and k-means) is a
fundamental machin...
We propose the first online quantum algorithm for zero-sum games with Õ(...
While quantum reinforcement learning (RL) has attracted a surge of atten...
Quantum algorithms for optimization problems are of general interest. De...
Recent results suggest that quantum computers possess the potential to s...
Estimating statistical properties is fundamental in statistics and compu...
Given a convex function fℝ^d→ℝ, the problem of
sampling from a distribut...
Classical algorithms are often not effective for solving nonconvex
optim...
We initiate the study of quantum algorithms for optimizing approximately...
Multi-arm bandit (MAB) and stochastic linear bandit (SLB) are important
...
Quantum simulation is a prominent application of quantum computers. Whil...
Escaping saddle points is a central research topic in nonconvex optimiza...
We study quantum algorithms that learn properties of a matrix using quer...
We investigate sublinear classical and quantum algorithms for matrix gam...
Let G = (V,w) be a weighted undirected graph with m edges. The cut
dimen...
We initiate the study of quantum algorithms for escaping from saddle poi...
Identifying the best arm of a multi-armed bandit is a central problem in...
The study of quantum generative models is well-motivated, not only becau...
We present an algorithmic framework generalizing quantum-inspired
polylo...
Estimating the volume of a convex body is a central problem in convex
ge...
We investigate quantum algorithms for classification, a fundamental prob...
A fundamental problem in statistics and learning theory is to test prope...
Semidefinite programming (SDP) is a central topic in mathematical
optimi...
While recent work suggests that quantum computers can speed up the solut...