Title: Probabilistic Quantum Error Mitigation via Adaptive Noise Injection

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum computing holds immense promise for revolutionary advancements across diverse fields, but its inherent sensitivity to noise necessitates sophisticated error mitigation strategies. Current approaches often rely on fixed, computationally intensive noise injection schemes, limiting scalability and requiring substantial overhead. This paper introduces a novel probabilistic error mitigation strategy leveraging real-time monitoring of quantum circuit error characteristics to dynamically adjust noise injection levels. The algorithm, termed Adaptive Noise Injection (ANI), continuously assesses the error using a simplified error metric and adjusts the noise injection accordingly. We demonstrate the effectiveness of ANI through simulations and preliminary experiments, achieving significant error reduction compared to static mitigation methods while maintaining high accuracy. This research addresses a critical limitation in quantum error mitigation, paving the way for more efficient and practical quantum computations.

DOI: 10.5281/zenodo.22501865

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