
Matrix hypercontractivity, streaming algorithms and LDCs: the large alphabet case
In this work, we prove a hypercontractive inequality for matrixvalued f...
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Private learning implies quantum stability
Learning an unknown nqubit quantum state ρ is a fundamental challenge i...
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Positive spectrahedra: Invariance principles and Pseudorandom generators
In a recent work, O'Donnell, Servedio and Tan (STOC 2019) gave explicit ...
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Quantum learning algorithms imply circuit lower bounds
We establish the first general connection between the design of quantum ...
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A rigorous and robust quantum speedup in supervised machine learning
Over the past few years several quantum machine learning algorithms were...
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Communication memento: Memoryless communication complexity
We study the communication complexity of computing functions F:{0,1}^n×{...
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Sampleefficient learning of quantum manybody systems
We study the problem of learning the Hamiltonian of a quantum manybody ...
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Quantum statistical query learning
We propose a learning model called the quantum statistical learning QSQ ...
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Quantum Boosting
Suppose we have a weak learning algorithm A for a Booleanvalued problem...
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The asymptotic induced matching number of hypergraphs: balanced binary strings
We compute the asymptotic induced matching number of the kpartite kuni...
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Quantum hardness of learning shallow classical circuits
In this paper we study the quantum learnability of constantdepth classi...
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Two new results about quantum exact learning
We present two new results about exact learning by quantum computers. Fi...
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Improved bounds on Fourier entropy and Minentropy
Given a Boolean function f:{1,1}^n→{1,1}, the Fourier distribution ass...
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Quantum Query Algorithms are Completely Bounded Forms
We prove a characterization of tquery quantum algorithms in terms of th...
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Optimizing quantum optimization algorithms via faster quantum gradient computation
We consider a generic framework of optimization algorithms based on grad...
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Srinivasan Arunachalam
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