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pro vyhledávání: '"Rietsch P"'
Autor:
Rietsch, Konstanze, Williams, Lauren
While mirror symmetry for flag varieties and Grassmannians has been extensively studied, Schubert varieties in the Grassmannian are singular, and hence standard mirror symmetry statements are not well-defined. Nevertheless, in this article we introdu
Externí odkaz:
http://arxiv.org/abs/2409.00734
Autor:
Rietsch, Konstanze, Williams, Lauren
In this paper we study the class of polytopes which can be obtained by taking the convex hull of some subset of the points $\{e_i-e_j \ \vert \ i \neq j\} \cup \{\pm e_i\}$ in $\mathbb{R}^n$, where $e_1,\dots,e_n$ is the standard basis of $\mathbb{R}
Externí odkaz:
http://arxiv.org/abs/2406.15803
Autor:
Rietsch, Sebastian, Dubey, Abhishek Y., Ufrecht, Christian, Periyasamy, Maniraman, Plinge, Axel, Mutschler, Christopher, Scherer, Daniel D.
This paper presents a deep reinforcement learning approach for synthesizing unitaries into quantum circuits. Unitary synthesis aims to identify a quantum circuit that represents a given unitary while minimizing circuit depth, total gate count, a spec
Externí odkaz:
http://arxiv.org/abs/2404.14865
Autor:
Rietsch, Konstanze
We give a definition of a what we call a `tonnetz' on a triangulated surface, generalising the famous tonnetz of Euler from 1739. In Euler's tonnetz the vertices of a regular `$A_2$ triangulation' of the plane are labelled with notes, or pitch-classe
Externí odkaz:
http://arxiv.org/abs/2401.15692
We construct a Pl\"ucker coordinate superpotential $\mathcal{F}_-$ that is mirror to a partial flag variety $\mathbb{ F}\ell(n_\bullet)$. Its Jacobi ring recovers the small quantum cohomology of $\mathbb{ F}\ell(n_\bullet)$ and we prove a folklore co
Externí odkaz:
http://arxiv.org/abs/2401.15640
Autor:
Ufrecht, Christian, Herzog, Laura S., Scherer, Daniel D., Periyasamy, Maniraman, Rietsch, Sebastian, Plinge, Axel, Mutschler, Christopher
Publikováno v:
Phys. Rev. A 109, 052440 (2024)
Circuit cutting, the partitioning of quantum circuits into smaller independent fragments, has become a promising avenue for scaling up current quantum-computing experiments. Here, we introduce a scheme for joint cutting of two-qubit rotation gates ba
Externí odkaz:
http://arxiv.org/abs/2312.09679
Autor:
Trishin, Sergey, Lotze, Christian, Richter, Johanna, Reecht, Gael, Krane, Nils, Rietsch, Philipp, Eigler, Siegfried, Franke, Katharina J.
Publikováno v:
Phys. Stat. Sol. A, 2300105 (2023)
Electrostatic potentials strongly affect molecular energy levels and charge states, providing the fascinating opportunity of molecular gating. Their influence on molecular vibrations remains less explored. Here, we investigate Ethyl-Diaminodicyanoqui
Externí odkaz:
http://arxiv.org/abs/2302.07816
Autor:
Ufrecht, Christian, Periyasamy, Maniraman, Rietsch, Sebastian, Scherer, Daniel D., Plinge, Axel, Mutschler, Christopher
Publikováno v:
Quantum 7, 1147 (2023)
Circuit cutting, the decomposition of a quantum circuit into independent partitions, has become a promising avenue towards experiments with larger quantum circuits in the noisy-intermediate scale quantum (NISQ) era. While previous work focused on cut
Externí odkaz:
http://arxiv.org/abs/2302.00387
Autor:
Merve Mutlu, Isabel Schmidt, Andrew I. Morrison, Benedikt Goretzki, Felix Freuler, Damien Begue, Oliver Simic, Nicolas Pythoud, Erik Ahrne, Sandra Kapps, Susan Roest, Debora Bonenfant, Delphine Jeanpierre, Thi-Thanh-Thao Tran, Rob Maher, Shaojian An, Amandine Rietsch, Florian Nigsch, Andreas Hofmann, John Reece-Hoyes, Christian N. Parker, Danilo Guerini
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-16 (2024)
Abstract Stimulator of interferon genes (STING) is a central component of the cytosolic nucleic acids sensing pathway and as such master regulator of the type I interferon response. Due to its critical role in physiology and its’ involvement in a v
Externí odkaz:
https://doaj.org/article/d64305878b7f4abe9dd92a3040e7b4f4
Reinforcement learning (RL) has shown to reach super human-level performance across a wide range of tasks. However, unlike supervised machine learning, learning strategies that generalize well to a wide range of situations remains one of the most cha
Externí odkaz:
http://arxiv.org/abs/2207.11432