Improved machine learning algorithm for predicting ground state properties

Understand

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 $c$.