Author Correction: Improved machine learning algorithm for predicting ground state properties

Finding the ground state of a quantum many-body system is a fundamental problem in quantum physics. In this work, we give a classical machine learning (ML) algorithm for predicting ground state properties with an inductive bias encoding geometric locality. The proposed ML model can efficiently predict ground state properties of an n -qubit gapped local Hamiltonian after learning from only 𝒪(log (n)) data about other Hamiltonians in the same quantum phase of matter. This improves substantially upon previous results that require 𝒪(n^c) data for a large constant c . Furthermore, the training and prediction time of the proposed ML model scale as 𝒪(nlog n) in the number of qubits n . Numerical experiments on physical systems with up to 45 qubits confirm the favorable scaling in predicting ground state properties using a small training dataset.