Search Results for author: Jean-Pierre Seifert

Found 7 papers, 1 papers with code

A super-polynomial quantum-classical separation for density modelling

no code implementations26 Oct 2022 Niklas Pirnay, Ryan Sweke, Jens Eisert, Jean-Pierre Seifert

Specifically, we (a) provide an overview of the relationships between hardness results in supervised learning and distribution learning, and (b) show that any weak pseudo-random function can be used to construct a classically hard density modelling problem.

A single $T$-gate makes distribution learning hard

no code implementations7 Jul 2022 Marcel Hinsche, Marios Ioannou, Alexander Nietner, Jonas Haferkamp, Yihui Quek, Dominik Hangleiter, Jean-Pierre Seifert, Jens Eisert, Ryan Sweke

We first show that the generative modelling problem associated with depth $d=n^{\Omega(1)}$ local quantum circuits is hard for any learning algorithm, classical or quantum.

Quantum Machine Learning

Learnability of the output distributions of local quantum circuits

no code implementations11 Oct 2021 Marcel Hinsche, Marios Ioannou, Alexander Nietner, Jonas Haferkamp, Yihui Quek, Dominik Hangleiter, Jean-Pierre Seifert, Jens Eisert, Ryan Sweke

As many practical generative modelling algorithms use statistical queries -- including those for training quantum circuit Born machines -- our result is broadly applicable and strongly limits the possibility of a meaningful quantum advantage for learning the output distributions of local quantum circuits.

Automatic Extraction of Secrets from the Transistor Jungle using Laser-Assisted Side-Channel Attacks

no code implementations23 Feb 2021 Thilo Krachenfels, Tuba Kiyan, Shahin Tajik, Jean-Pierre Seifert

In this work, we present a novel approach that can extract the secret key without any knowledge of the IC's layout, and independent from the employed memory technology as key storage.

Cryptography and Security

On the Quantum versus Classical Learnability of Discrete Distributions

no code implementations28 Jul 2020 Ryan Sweke, Jean-Pierre Seifert, Dominik Hangleiter, Jens Eisert

Here we study the comparative power of classical and quantum learners for generative modelling within the Probably Approximately Correct (PAC) framework.

Static Exploration of Taint-Style Vulnerabilities Found by Fuzzing

no code implementations1 Jun 2017 Bhargava Shastry, Federico Maggi, Fabian Yamaguchi, Konrad Rieck, Jean-Pierre Seifert

In this paper, we use static template matching to find recurrences of fuzzer-discovered vulnerabilities.

Cryptography and Security Programming Languages Software Engineering

Practical Attacks Against Privacy and Availability in 4G/LTE Mobile Communication Systems

1 code implementation26 Oct 2015 Altaf Shaik, Ravishankar Borgaonkar, N. Asokan, Valtteri Niemi, Jean-Pierre Seifert

We carefully analyzed LTE access network protocol specifications and uncovered several vulnerabilities.

Cryptography and Security

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