Quantum Machine Learning via Causal Inference

Jincheng Zhang

Published Aug 28, 2026• 0 citations

Abstract

This paper explores the potential of quantum machine learning to revolutionize causal inference within machine learning models. Traditional machine learning often operates as a "black box," lacking transparency regarding the underlying causal relationships driving predictions. We propose a novel framework leveraging quantum mechanics – specifically quantum entanglement and superposition – to achieve precise causal inference. This framework centers around simulating causal relationships using quantum circuits, validating causal effects through quantum measurements, and mitigating measurement errors with quantum error correction. The core claim is that this approach can overcome the limitations of classical methods and provide a fundamentally more accurate and interpretable understanding of complex systems. We outline the key components of this framework, emphasizing the theoretical foundations and potential challenges, and argue for a significant paradigm shift in how we approach causal discovery and model validation within the field of machine learning. The ultimate goal is to build inherently causal quantum machine learning models that are robust to noise and offer unparalleled predictive power. ---

DOI: 10.5281/zenodo.22140333

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