##基于多变量因果推断的量子算法优化

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper investigates the optimization of quantum algorithms by leveraging the principles of causal inference. Traditional quantum algorithm optimization often relies on heuristic methods or black-box approaches, lacking the transparency and robustness required for complex systems. We propose a novel framework that integrates causal inference with quantum algorithm design, aiming to identify and mitigate critical factors influencing algorithm performance. The core of our approach involves constructing a causal model of the quantum algorithm, represented as a causal Bayesian network. This network allows us to explicitly model dependencies between algorithmic components, enabling targeted interventions to improve efficiency and accuracy. We demonstrate the potential of this approach through a theoretical analysis, outlining a methodology for identifying confounding variables and implementing causal interventions. The resulting framework offers a more interpretable and reliable path towards optimizing quantum algorithms, particularly in scenarios where understanding underlying causal relationships is paramount.

DOI: 10.5281/zenodo.22474158

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