Zobrazeno 1 - 10
of 279
pro vyhledávání: '"P Schatzki"'
Autor:
Schatzki, Louis
In this short note we show that the ensemble $\{O \vert 0\rangle \langle 0 \vert O^\top \ \vert \ O \in \mathbb{O(d)}\}$, where $O$ is drawn from the Haar measure on $\mathbb{O}(d)$ cannot be distinguished from $t$ copies of a Haar random state unles
Externí odkaz:
http://arxiv.org/abs/2410.17213
We consider the task of distributed inner product estimation when allowed limited quantum communication. Here, Alice and Bob are given $k$ copies of an unknown $n$-qubit quantum states $\vert \psi \rangle,\vert \phi \rangle$ respectively. They are al
Externí odkaz:
http://arxiv.org/abs/2410.12684
In this work we make progress in understanding the relationship between learning models with access to entangled, separable and statistical measurements in the quantum statistical query (QSQ) model. To this end, we show the following results. $\textb
Externí odkaz:
http://arxiv.org/abs/2306.03161
Publikováno v:
npj Quantum Inf 10, 12 (2024)
Despite the great promise of quantum machine learning models, there are several challenges one must overcome before unlocking their full potential. For instance, models based on quantum neural networks (QNNs) can suffer from excessive local minima an
Externí odkaz:
http://arxiv.org/abs/2210.09974
Autor:
Nguyen, Quynh T., Schatzki, Louis, Braccia, Paolo, Ragone, Michael, Coles, Patrick J., Sauvage, Frederic, Larocca, Martin, Cerezo, M.
Publikováno v:
PRX Quantum 5, 020328 (2024)
Quantum neural network architectures that have little-to-no inductive biases are known to face trainability and generalization issues. Inspired by a similar problem, recent breakthroughs in machine learning address this challenge by creating models e
Externí odkaz:
http://arxiv.org/abs/2210.08566
Autor:
Ragone, Michael, Braccia, Paolo, Nguyen, Quynh T., Schatzki, Louis, Coles, Patrick J., Sauvage, Frederic, Larocca, Martin, Cerezo, M.
Recent advances in classical machine learning have shown that creating models with inductive biases encoding the symmetries of a problem can greatly improve performance. Importation of these ideas, combined with an existing rich body of work at the n
Externí odkaz:
http://arxiv.org/abs/2210.07980
Publikováno v:
Phys. Rev. Research 6, 023019 (2024)
Multipartite entanglement is one of the hallmarks of quantum mechanics and is central to quantum information processing. In this work we show that Concentratable Entanglement (CE), an operationally motivated entanglement measure, induces a hierarchy
Externí odkaz:
http://arxiv.org/abs/2209.07607
Graph states play an important role in quantum information theory through their connection to measurement-based computing and error correction. Prior work has revealed elegant connections between the graph structure of these states and their multipar
Externí odkaz:
http://arxiv.org/abs/2209.06320
High-quality, large-scale datasets have played a crucial role in the development and success of classical machine learning. Quantum Machine Learning (QML) is a new field that aims to use quantum computers for data analysis, with the hope of obtaining
Externí odkaz:
http://arxiv.org/abs/2109.03400
Publikováno v:
npj Quantum Information, Vol 10, Iss 1, Pp 1-14 (2024)
Abstract Despite the great promise of quantum machine learning models, there are several challenges one must overcome before unlocking their full potential. For instance, models based on quantum neural networks (QNNs) can suffer from excessive local
Externí odkaz:
https://doaj.org/article/544fa5184c2146dd91386ed3571ce267
