  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 $\mathcal{O}(\log(n))$ data about other Hamiltonians in the same
quantum phase of matter. This improves substantially upon previous results that
require $\mathcal{O}(n^c)$ data for a large constant $c$. Furthermore, the
training and prediction time of the proposed ML model scale as $\mathcal{O}(n
\log n)$ 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.
