Ising machines are hardware solvers that aim to find the absolute or approximate ground
states of the Ising model. The Ising model is of fundamental computational interest because …

Community detection in networks is one of the most popular topics of modern network
science. Communities, or clusters, are usually groups of vertices having higher probability of …

Widely observed neural scaling laws, in which error falls off as a power of the training set
size, model size, or both, have driven substantial performance improvements in deep …

This is a review of the Sachdev-Ye-Kitaev (SYK) model of compressible quantum many-
body systems without quasiparticle excitations, and its connections to various theoretical …

The modern science of networks has brought significant advances to our understanding of
complex systems. One of the most relevant features of graphs representing real systems is …

We provide Ising formulations for many NP-complete and NP-hard problems, including all of
Karp's 21 NP-complete problems. This collects and extends map**s to the Ising model …

We review the dynamical mean-field theory of strongly correlated electron systems which is
based on a map** of lattice models onto quantum impurity models subject to a self …

Many modern unsupervised or semi-supervised machine learning algorithms rely on
Bayesian probabilistic models. These models are usually intractable and thus require …

This review summarizes recent developments in the theory of spin glasses, as well as
pertinent experimental data. The most characteristic properties of spin glass systems are …

With the high-temperature superconductors a qualitatively new regime in the
phenomenology of type-II superconductivity can be accessed. The key elements governing …