Model-based framework for automated quantification of error sources using quantum state tomography data

Abstract

High-quality quantum state generation is essential for advanced quantum information processing, including quantum communication, quantum sensing, and quantum computing. In realistic experiments, however, the generated quantum state is degraded by multiple experimental error sources whose individual contributions are often difficult to quantify and calibrate. Quantum state tomography (QST) provides experimental access to the resulting density matrix, but it does not by itself attribute observed deviations to specific error sources. To address this problem, we propose an automated method for quantifying error sources by combining simulation and parameter optimization, in which a parametrized simulator of state preparation and measurement is optimized so that the simulated density matrix best fits the experimentally reconstructed density matrix obtained via QST. We focus on the experimental generation of time-bin entangled photon pairs, for which we model the relevant error sources and simulate the density matrix with adjustable model parameters, thereby optimizing the parameters and minimizing the trace distance to the experimental data. Optimization of the parameters reduced the trace distance from 0.177 to 0.024, indicating that our modeled error sources explain 86% of the errors. Reducing the predicted error sources improves the state quality, consistent with our predictions and thus validating the proposed method. The proposed framework provides a practical route to automated, quantitative error attribution and calibration directly from experimental QST data.