Data-enhanced variational Monte Carlo for Rydberg atom arrays

          @misc{ pirsa_PIRSA:22050042,
            doi = {10.48660/22050042},
            url = {https://pirsa.org/22050042},
            author = {Czischek, Stefanie},
            keywords = {Condensed Matter},
            language = {en},
            title = {Data-enhanced variational Monte Carlo for Rydberg atom arrays},
            publisher = {Perimeter Institute},
            year = {2022},
            month = {may},
            note = {PIRSA:22050042 see, \url{https://pirsa.org}}
          }
          

Abstract

Rydberg atom arrays are programmable quantum simulators capable of preparing interacting qubit systems in a variety of quantum states. However, long experimental state preparation times limit the amount of measurement data that can be generated at reasonable timescales, posing a challenge for 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 wavefunctions. These models can be efficiently trained from projective measurement data or through Hamiltonian-guided variational Monte Carlo. In this talk, I will compare the data-driven and Hamiltonian-driven training procedures to reconstruct ground states of two-dimensional Rydberg atom arrays. I will discuss the limitations of both approaches and demonstrate how pretraining on a small amount of measurement data can significantly reduce the convergence time for a subsequent variational optimization of the wavefunction.