



01923nas a2200241   4500008004100000245007300041210006900114260001400183520110600197653004301303653002701346653002901373653003901402653003101441653005201472100002001524700002801544700001501572700002401587700001601611700001701627856003701644       2022                        eng d00aScalably learning quantum many-body Hamiltonians from dynamical data0 aScalably learning quantum manybody Hamiltonians from dynamical d  c9/28/20223 a<p>The physics of a closed quantum mechanical system is governed by its Hamiltonian. However, in most practical situations, this Hamiltonian is not precisely known, and ultimately all there is are data obtained from measurements on the system. In this work, we introduce a highly scalable, data-driven approach to learning families of interacting many-body Hamiltonians from dynamical data, by bringing together techniques from gradient-based optimization from machine learning with efficient quantum state representations in terms of tensor networks. Our approach is highly practical, experimentally friendly, and intrinsically scalable to allow for system sizes of above 100 spins. In particular, we demonstrate on synthetic data that the algorithm works even if one is restricted to one simple initial state, a small number of single-qubit observables, and time evolution up to relatively short times. For the concrete example of the one-dimensional Heisenberg model our algorithm exhibits an error constant in the system size and scaling as the inverse square root of the size of the data set.</p>10aFOS: Computer and information sciences10aFOS: Physical sciences10aMachine Learning (cs.LG)10aQuantum Gases (cond-mat.quant-gas)10aQuantum Physics (quant-ph)10aStrongly Correlated Electrons (cond-mat.str-el)1 aWilde, Frederik1 aKshetrimayum, Augustine1 aRoth, Ingo1 aHangleiter, Dominik1 aSweke, Ryan1 aEisert, Jens  uhttps://arxiv.org/abs/2209.14328