Quantum learning theory is a new and very active area of research at the intersection of
quantum computing and machine learning. Important breakthroughs in the past two years …

Inverse problems in statistical physics are motivated by the challenges of 'big data'in
different fields, in particular high-throughput experiments in biology. In inverse problems, the …

Learning the Hamiltonian that describes interactions in a quantum system is an important
task in both condensed-matter physics and the verification of quantum technologies. Its …

arXiv:2310.19244v1 [math.ST] 30 Oct 2023 Page 1 arXiv:2310.19244v1 [math.ST] 30 Oct
2023 High-Dimensional Statistics Lecture Notes Philippe Rigollet and Jan-Christian Hütter …

We study the problem of learning a Hamiltonian H to precision ε, supposing we are given
copies of its Gibbs state ρ=\exp(-βH)/Tr(\exp(-βH)) at a known inverse temperature β. Anshu …

We give a simple, multiplicative-weight update algorithm for learning undirected graphical
models or Markov random fields (MRFs). The approach is new, and for the well-studied case …

Given samples from an unknown multivariate distribution p, is it possible to distinguish
whether p is the product of its marginals versus p being far from every product distribution …

The behaviour of a system is determined by its Hamiltonian. In many cases, the exact
Hamiltonian is not known and has to be extracted by analysing the outcome of …

Reconstruction of the structure and parameters of an Ising model from binary samples is a
problem of practical importance in a variety of disciplines, ranging from statistical physics …

Estimating physical properties of quantum states from measurements is one of the most
fundamental tasks in quantum science. In this work, we identify conditions on states under …