In this work we make progress in understanding the relationship between
...
We study the advantages of quantum communication models over classical
c...
We survey various recent results that rigorously study the complexity of...
Performance-based engineering for natural hazards facilitates the design...
The simulation of stochastic wind loads is necessary for many applicatio...
We present a linear program for the one-way version of the partition bou...
We analyze the complexity of learning n-qubit quantum phase states. A
de...
As inelastic design for wind is embraced by the engineering community, t...
We initiate the study of parameterized complexity of problems
in terms ...
In this work, we prove a hypercontractive inequality for matrix-valued
f...
Learning an unknown n-qubit quantum state ρ is a fundamental challenge
i...
In a recent work, O'Donnell, Servedio and Tan (STOC 2019) gave explicit
...
We establish the first general connection between the design of quantum
...
Over the past few years several quantum machine learning algorithms were...
We study the communication complexity of computing functions
F:{0,1}^n×{...
We study the problem of learning the Hamiltonian of a quantum many-body
...
We propose a learning model called the quantum statistical learning QSQ
...
Suppose we have a weak learning algorithm A for a Boolean-valued
problem...
We compute the asymptotic induced matching number of the k-partite
k-uni...
In this paper we study the quantum learnability of constant-depth classi...
We present two new results about exact learning by quantum computers. Fi...
Given a Boolean function f:{-1,1}^n→{-1,1}, the Fourier distribution
ass...
We prove a characterization of t-query quantum algorithms in terms of th...
We consider a generic framework of optimization algorithms based on grad...