Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Kiani, Bobak"'
Preparing thermal (Gibbs) states is a common task in physics and computer science. Recent algorithms mimic cooling via system-bath coupling, where the cost is determined by mixing time, akin to classical Metropolis-like algorithms. However, few metho
Externí odkaz:
http://arxiv.org/abs/2411.04300
Data with geometric structure is ubiquitous in machine learning often arising from fundamental symmetries in a domain, such as permutation-invariance in graphs and translation-invariance in images. Group-convolutional architectures, which encode symm
Externí odkaz:
http://arxiv.org/abs/2410.05499
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
The manifold hypothesis presumes that high-dimensional data lies on or near a low-dimensional manifold. While the utility of encoding geometric structure has been demonstrated empirically, rigorous analysis of its impact on the learnability of neural
Externí odkaz:
http://arxiv.org/abs/2406.01461
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
We study the problem of learning equivariant neural networks via gradient descent. The incorporation of known symmetries ("equivariance") into neural nets has empirically improved the performance of learning pipelines, in domains ranging from biology
Externí odkaz:
http://arxiv.org/abs/2401.01869
Autor:
Kiani, Bobak T.
The potential emergence of practical quantum computers has guided research into their potential applications, particularly in the context of artificial intelligence. Motivated by the success of deep neural networks in classical machine learning, a pr
Externí odkaz:
https://hdl.handle.net/1721.1/151436
We consider the problem of estimating the maximal energy of quantum $p$-local spin glass random Hamiltonians, the quantum analogues of widely studied classical spin glass models. Denoting by $E^*(p)$ the (appropriately normalized) maximal energy in t
Externí odkaz:
http://arxiv.org/abs/2309.11709
Quantum machine learning has the potential to enable advances in artificial intelligence, such as solving problems intractable on classical computers. Some fundamental ideas behind quantum machine learning are similar to kernel methods in classical m
Externí odkaz:
http://arxiv.org/abs/2308.06807
Autor:
Mialon, Grégoire, Garrido, Quentin, Lawrence, Hannah, Rehman, Danyal, LeCun, Yann, Kiani, Bobak T.
Machine learning for differential equations paves the way for computationally efficient alternatives to numerical solvers, with potentially broad impacts in science and engineering. Though current algorithms typically require simulated training data
Externí odkaz:
http://arxiv.org/abs/2307.05432