Meta-variational quantum Monte Carlo

Motivated by close analogies between meta-reinforcement learning (Meta-RL) and variational quantum Monte Carlo with disorder, we propose a learning problem and an associated notion of generalization, with applications in ground state determination for quantum systems described by random Hamiltonians. Specifically, we elaborate on a proposal of (Zhao et al. 2020b ) interpreting the Hamiltonian disorder as task uncertainty for a Meta-RL agent. A model-agnostic meta-learning approach is proposed to solve the associated learning problem and numerical experiments in disordered quantum spin systems indicate that the resulting meta-variational Monte Carlo accelerates training and improves converged energies.