Quantum process tomography with unsupervised learning and tensor networks.

ABSTRACT: The impressive pace of advance of quantum technology calls for robust and scalable techniques for the characterization and validation of quantum hardware. Quantum process tomography, the reconstruction of an unknown quantum channel from measurement data, remains the quintessential primitive to completely characterize quantum devices. However, due to the exponential scaling of the required data and classical post-processing, its range of applicability is typically restricted to one- and two-qubit gates. Here, we present a technique for performing quantum process tomography that addresses these issues by combining a tensor network representation of the channel with a data-driven optimization inspired by unsupervised machine learning. We demonstrate our technique through synthetically generat

SUBMITTER: Torlai G 

PROVIDER: S-EPMC10199030 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature