Synergistic pretraining of parametrized quantum circuits via tensor networks.

ABSTRACT: Parametrized quantum circuits (PQCs) represent a promising framework for using present-day quantum hardware to solve diverse problems in materials science, quantum chemistry, and machine learning. We introduce a "synergistic" approach that addresses two prominent issues with these models: the prevalence of barren plateaus in PQC optimization landscapes, and the difficulty to outperform state-of-the-art classical algorithms. This framework first uses classical resources to compute a tensor network encoding a high-quality solution, and then converts this classical output into a PQC which can be further improved using quantum resources. We provide numerical evidence that this framework effectively mitigates barren plateaus in systems of up to 100 qubits using only moderate classical resources

SUBMITTER: Rudolph MS 

PROVIDER: S-EPMC10724286 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature