no code implementations • NeurIPS 2023 • Srinivasan Arunachalam, Vojtech Havlicek, Louis Schatzki
We exhibit a class $C$ that gives an exponential separation between QSQ learning and quantum learning with entangled measurements (even in the presence of noise).
no code implementations • 18 Oct 2022 • Louis Schatzki, Martin Larocca, Quynh T. Nguyen, Frederic Sauvage, M. Cerezo
Despite the great promise of quantum machine learning models, there are several challenges one must overcome before unlocking their full potential.
no code implementations • 16 Oct 2022 • Quynh T. Nguyen, Louis Schatzki, Paolo Braccia, Michael Ragone, Patrick J. Coles, Frederic Sauvage, Martin Larocca, M. Cerezo
Inspired by a similar problem, recent breakthroughs in machine learning address this challenge by creating models encoding the symmetries of the learning task.
no code implementations • 14 Oct 2022 • Michael Ragone, Paolo Braccia, Quynh T. Nguyen, Louis Schatzki, Patrick J. Coles, Frederic Sauvage, Martin Larocca, M. Cerezo
Recent advances in classical machine learning have shown that creating models with inductive biases encoding the symmetries of a problem can greatly improve performance.
1 code implementation • 8 Sep 2021 • Louis Schatzki, Andrew Arrasmith, Patrick J. Coles, M. Cerezo
For this purpose, we introduce the NTangled dataset composed of quantum states with different amounts and types of multipartite entanglement.