Improved machine learning algorithm for predicting ground state properties
Laura Lewis,
Hsin-Yuan Huang,
Viet T. Tran
et al.
Abstract: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 $${{{{{{{\mathcal{O}}}}}}}}(\log (n))$$
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