SKM 2023 – wissenschaftliches Programm

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TT: Fachverband Tiefe Temperaturen

TT 2: Focus Session: Physics Meets ML I – Machine Learning for Complex Quantum Systems (joint session TT/DY)

TT 2.1: Hauptvortrag

Montag, 27. März 2023, 09:30–10:00, HSZ 03

Enhanced variational Monte Carlo for Rydberg atom arrays — •Stefanie Czischek — Department of Physics, University of Ottawa, Ottawa, Canada, K1N 6N6

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.

While both approaches experience individual limitations, 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. Additional specific modifications of the neural network model and its implementation can further optimize the performance of variational Monte Carlo simulations for Rydberg atom arrays and provide significant insights into their behaviour.