Zobrazeno 1 - 10
of 1 800
pro vyhledávání: '"A. Anschütz"'
Explanatory images play a pivotal role in accessible and easy-to-read (E2R) texts. However, the images available in online databases are not tailored toward the respective texts, and the creation of customized images is expensive. In this large-scale
Externí odkaz:
http://arxiv.org/abs/2410.03430
Random spin systems at low temperatures are glassy and feature computational hardness in finding low-energy states. We study the random all-to-all interacting fermionic Sachdev--Ye--Kitaev (SYK) model and prove that, in contrast, (I) the low-energy s
Externí odkaz:
http://arxiv.org/abs/2408.15699
Autor:
Anschuetz, Eric R.
Classical neural networks with random initialization famously behave as Gaussian processes in the limit of many neurons, which allows one to completely characterize their training and generalization behavior. No such general understanding exists for
Externí odkaz:
http://arxiv.org/abs/2408.11901
Autor:
Appel, Christian, Schmeltz, Margaux, Rodriguez-Fernandez, Irene, Anschuetz, Lukas, Nielsen, Leonard C., Panepucci, Ezequiel, Marijolovic, Tomislav, Wakonig, Klaus, Ivanovic, Aleksandra, Bonnin, Anne, Leonarski, Filip, Wojdyla, Justyna, Tomizaki, Takashi, Guizar-Sicairos, Manuel, Smith, Kate, Beale, John H., Glettig, Wayne, McAuley, Katherine, Bunk, Oliver, Wang, Meitian, Liebi, Marianne
Small Angle-X-ray Scattering Tensor Tomography (SAS-TT) is a relatively new, but powerful technique for studying the multiscale architecture of hierarchical structures, which is of particular interest for life science applications. Currently, the tec
Externí odkaz:
http://arxiv.org/abs/2406.13238
Text simplification seeks to improve readability while retaining the original content and meaning. Our study investigates whether pre-trained classifiers also maintain such coherence by comparing their predictions on both original and simplified inpu
Externí odkaz:
http://arxiv.org/abs/2404.06838
We consider the problem of estimating the ground state energy of quantum $p$-local spin glass random Hamiltonians, the quantum analogues of widely studied classical spin glass models. Our main result shows that the maximum energy achievable by produc
Externí odkaz:
http://arxiv.org/abs/2404.07231
Autor:
Perlin, Michael A., Shaydulin, Ruslan, Hall, Benjamin P., Minssen, Pierre, Li, Changhao, Dubey, Kabir, Rines, Rich, Anschuetz, Eric R., Pistoia, Marco, Gokhale, Pranav
Combinatorial optimization problems that arise in science and industry typically have constraints. Yet the presence of constraints makes them challenging to tackle using both classical and quantum optimization algorithms. We propose a new quantum alg
Externí odkaz:
http://arxiv.org/abs/2403.05653
Autor:
Anschütz, Johannes, Mann, Lucas
We prove $v$-descent for solid quasi-coherent sheaves on perfectoid spaces as a key technical input for the development of a $6$-functor formalism with values in solid quasi-coherent sheaves on relative Fargues--Fontaine curves.
Comment: 67 page
Comment: 67 page
Externí odkaz:
http://arxiv.org/abs/2403.01951
Autor:
Anschuetz, Eric R., Gao, Xun
Recent theoretical results in quantum machine learning have demonstrated a general trade-off between the expressive power of quantum neural networks (QNNs) and their trainability; as a corollary of these results, practical exponential separations in
Externí odkaz:
http://arxiv.org/abs/2402.08606
Autor:
Cerezo, M., Larocca, Martin, García-Martín, Diego, Diaz, N. L., Braccia, Paolo, Fontana, Enrico, Rudolph, Manuel S., Bermejo, Pablo, Ijaz, Aroosa, Thanasilp, Supanut, Anschuetz, Eric R., Holmes, Zoë
A large amount of effort has recently been put into understanding the barren plateau phenomenon. In this perspective article, we face the increasingly loud elephant in the room and ask a question that has been hinted at by many but not explicitly add
Externí odkaz:
http://arxiv.org/abs/2312.09121