233 citations · 531 across the 32 of their papers we have counts for
7 papers · 1 filter
Product states optimize quantum -spin models for large
Eric R. Anschuetz, David Gamarnik, Bobak T. Kiani
We consider the problem of estimating the maximal energy of quantum -local spin glass random Hamiltonians, the quantum analogues of widely studied classical spin glass models. D…
Neural Networks for Programming Quantum Annealers
Samuel Bosch, Bobak Kiani, Rui Yang +2
Quantum machine learning has the potential to enable advances in artificial intelligence, such as solving problems intractable on classical computers. Some fundamental ideas behind…
Self-Supervised Learning with Lie Symmetries for Partial Differential Equations
Grégoire Mialon, Quentin Garrido, Hannah Lawrence +3
Machine learning for differential equations paves the way for computationally efficient alternatives to numerical solvers, with potentially broad impacts in science and engineering…
Combinatorial NLTS From the Overlap Gap Property
Eric R. Anschuetz, David Gamarnik, Bobak Kiani
In an important recent development, Anshu, Breuckmann, and Nirkhe [ABN22] resolved positively the so-called No Low-Energy Trivial State (NLTS) conjecture by Freedman and Hastings.…
Equivariant Polynomials for Graph Neural Networks
Omri Puny, Derek Lim, Bobak T. Kiani +2
Graph Neural Networks (GNN) are inherently limited in their expressive power. Recent seminal works (Xu et al., 2019; Morris et al., 2019b) introduced the Weisfeiler-Lehman (WL) hie…
The SSL Interplay: Augmentations, Inductive Bias, and Generalization
Vivien Cabannes, Bobak T. Kiani, Randall Balestriero +2
Self-supervised learning (SSL) has emerged as a powerful framework to learn representations from raw data without supervision. Yet in practice, engineers face issues such as instab…