Title: Adaptive Bias-Corrected Quantum State Reconstruction

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Abstract: Quantum state reconstruction is a fundamental problem in quantum information processing, crucial for applications ranging from quantum simulation to quantum key distribution. However, the inherent sensitivity of quantum states to measurement introduces a significant challenge – bias, which can severely degrade reconstruction accuracy. This paper introduces an adaptive bias-corrected quantum state reconstruction method, aiming to mitigate this bias through a novel iterative correction process. We propose a framework that utilizes a learned model to dynamically adjust the correction parameters, optimizing for statistical accuracy and minimizing the impact of systematic errors. The method is designed to address the limitations of existing techniques, offering a pathway towards more reliable and precise state reconstruction, particularly in scenarios involving complex measurement environments.

DOI: 10.5281/zenodo.22509526

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