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31. March 2022 11:15

Data Enhanced Neural Network Training to Find Ground States of Rydberg Atom Arrays

Dr. Stefanie Czischek
University of Waterloo, Canada

Rydberg atom arrays are promising candidates for high-quality quantum computation and quantum simulation. However, long state preparation times limit the amount of measurement data that can be generated at reasonable timescales. This restriction directly affects the estimation of operator expectation values, as well as the reconstruction and characterization of quantum states. Over the last years, neural networks have been explored as a powerful and systematically tuneable ansatz to represent quantum wave functions. Via tomographical state reconstruction, such numerical models can significantly reduce the amount of necessary measurements to accurately reconstruct operator expectation values. At the same time, neural networks can find ground state wave functions of given Hamiltonians via variational energy minimization. In this talk, I will apply both the data-driven and the Hamiltonian-driven training procedures to reconstruct the ground state of a two-dimensional array of Rydberg atoms in the vicinity of a quantum phase transition. I will demonstrate the limitations of the individual approaches and show that a combination of the two leads to a significant enhancement in the variational ground state search by naturally finding an improved network initialization from a limited amount of measurement data.