no code implementations • 22 Feb 2024 • Nicola Mariella, Albert Akhriev, Francesco Tacchino, Christa Zoufal, Juan Carlos Gonzalez-Espitia, Benedek Harsanyi, Eugene Koskin, Ivano Tavernelli, Stefan Woerner, Marianna Rapsomaniki, Sergiy Zhuk, Jannis Born

Optimal Transport (OT) has fueled machine learning (ML) across many domains.

no code implementations • 19 Apr 2023 • Gian Gentinetta, David Sutter, Christa Zoufal, Bryce Fuller, Stefan Woerner

Specifically, we demonstrate that Pegasos is particularly effective for non-stationary data, which is an important challenge in real-world applications.

no code implementations • 28 Feb 2022 • Gian Gentinetta, Arne Thomsen, David Sutter, Stefan Woerner

We show that the dual problem can be solved in $O(M^{4. 67}/\varepsilon^2)$ quantum circuit evaluations, where $M$ denotes the size of the data set and $\varepsilon$ the solution accuracy compared to the ideal result from exact expectation values, which is only obtainable in theory.

1 code implementation • 9 Dec 2021 • Amira Abbas, David Sutter, Alessio Figalli, Stefan Woerner

Making statements about the performance of trained models on tasks involving new data is one of the primary goals of machine learning, i. e., to understand the generalization power of a model.

2 code implementations • 30 Oct 2020 • Amira Abbas, David Sutter, Christa Zoufal, Aurélien Lucchi, Alessio Figalli, Stefan Woerner

We show that quantum neural networks are able to achieve a significantly better effective dimension than comparable classical neural networks.

1 code implementation • 11 Sep 2019 • Raban Iten, Romain Moyard, Tony Metger, David Sutter, Stefan Woerner

An important building block for many quantum circuit optimization techniques is pattern matching, where given a large and a small quantum circuit, we are interested in finding all maximal matches of the small circuit, called pattern, in the large circuit, considering pairwise commutation of quantum gates.

Quantum Physics Data Structures and Algorithms

2 code implementations • 10 Jul 2019 • Panagiotis Kl. Barkoutsos, Giacomo Nannicini, Anton Robert, Ivano Tavernelli, Stefan Woerner

The expectation is estimated as the sample mean of a set of measurement outcomes, while the parameters of the trial state are optimized classically.

Quantum Physics

no code implementations • npj Quantum Information 2019 • Christa Zoufal, Aurélien Lucchi, Stefan Woerner

Through the interplay of a quantum channel, such as a variational quantum circuit, and a classical neural network, the qGAN can learn a representation of the probability distribution underlying the data samples and load it into a quantum state.

Quantum Physics

1 code implementation • 18 Jun 2018 • Stefan Woerner, Daniel J. Egger

Additionally, we show how to implement this algorithm and how to trade off the convergence rate of the algorithm and the circuit depth.

Quantum Physics

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