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Found 12 papers, 3 papers with code
Lower Bounds on Learning Pauli Channels
In the adaptive setting, we show a lower bound of $\Omega(2^{2. 5n}\epsilon^{-2})$ for $\epsilon=\mathcal{O}(2^{-n})$, and a lower bound of $\Omega(2^{2n}\epsilon^{-2} )$ for any $\epsilon > 0$.
Sequential Algorithms for Testing Closeness of Distributions
For a general alphabet size $n$, we give a sequential algorithm that uses no more samples than its batch counterpart, and possibly fewer if the actual distance between $\mathcal{D}_1$ and $\mathcal{D}_2$ is larger than $\epsilon$.
Quasi-polynomial time algorithms for free quantum games in bounded dimension
We give a converging semidefinite programming hierarchy of outer approximations for the set of quantum correlations of fixed dimension and derive analytical bounds on the convergence speed of the hierarchy.
Quantum Physics
Learning dynamic polynomial proofs
In this work, we introduce a machine learning based method to search for a dynamic proof within these proof systems.
Entropy accumulation with improved second-order
The entropy accumulation theorem states that the smooth min-entropy of an $n$-partite system $A = (A_1, \ldots, A_n)$ is lower-bounded by the sum of the von Neumann entropies of suitably chosen conditional states up to corrections that are sublinear in $n$.
Quantum Physics
Adversarial vulnerability for any classifier
Despite achieving impressive performance, state-of-the-art classifiers remain highly vulnerable to small, imperceptible, adversarial perturbations.
Robustness of classifiers to uniform $\ell\_p$ and Gaussian noise
We study the robustness of classifiers to various kinds of random noise models.
Robustness of classifiers to universal perturbations: a geometric perspective
Deep networks have recently been shown to be vulnerable to universal perturbations: there exist very small image-agnostic perturbations that cause most natural images to be misclassified by such classifiers.
Universal adversarial perturbations
Given a state-of-the-art deep neural network classifier, we show the existence of a universal (image-agnostic) and very small perturbation vector that causes natural images to be misclassified with high probability.
Entropy accumulation
We ask the question whether entropy accumulates, in the sense that the operationally relevant total uncertainty about an $n$-partite system $A = (A_1, \ldots A_n)$ corresponds to the sum of the entropies of its parts $A_i$.
Quantum Physics Information Theory Information Theory
Analysis of classifiers' robustness to adversarial perturbations
To the best of our knowledge, our results provide the first theoretical work that addresses the phenomenon of adversarial instability recently observed for deep networks.
Quantum to Classical Randomness Extractors
Yet, when considering a physical randomness source, X is itself ultimately the result of a measurement on an underlying quantum system.
Quantum Physics
