104 citations · 325 across the 25 of their papers we have counts for
7 papers · 1 filter
An unconditional distribution learning advantage with shallow quantum circuits
N. Pirnay, S. Jerbi, J. -P. Seifert +1
One of the core challenges of research in quantum computing is concerned with the question whether quantum advantages can be found for near-term quantum circuits that have implicat…
Good Gottesman-Kitaev-Preskill codes from the NTRU cryptosystem
Jonathan Conrad, Jens Eisert, Jean-Pierre Seifert
We introduce a new class of random Gottesman-Kitaev-Preskill (GKP) codes derived from the cryptanalysis of the so-called NTRU cryptosystem. The derived codes are good in that they…
An in-principle super-polynomial quantum advantage for approximating combinatorial optimization problems via computational learning theory
Niklas Pirnay, Vincent Ulitzsch, Frederik Wilde +2
Combinatorial optimization - a field of research addressing problems that feature strongly in a wealth of scientific and industrial contexts - has been identified as one of the cor…
A super-polynomial quantum-classical separation for density modelling
Niklas Pirnay, Ryan Sweke, Jens Eisert +1
Density modelling is the task of learning an unknown probability density function from samples, and is one of the central problems of unsupervised machine learning. In this work, w…
Learning Classical Readout Quantum PUFs based on single-qubit gates
Niklas Pirnay, Anna Pappa, Jean-Pierre Seifert
Physical Unclonable Functions (PUFs) have been proposed as a way to identify and authenticate electronic devices. Recently, several ideas have been presented that aim to achieve th…
Learnability of the output distributions of local quantum circuits
Marcel Hinsche, Marios Ioannou, Alexander Nietner +6
There is currently a large interest in understanding the potential advantages quantum devices can offer for probabilistic modelling. In this work we investigate, within two differe…
On the Quantum versus Classical Learnability of Discrete Distributions
Ryan Sweke, Jean-Pierre Seifert, Dominik Hangleiter +1
Here we study the comparative power of classical and quantum learners for generative modelling within the Probably Approximately Correct (PAC) framework. More specifically we consi…