{"ID":2878220,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.19327","arxiv_id":"2508.19327","title":"Quantum Entanglement as Super-Confounding: From Bell's Theorem to Robust Machine Learning","abstract":"Bell's theorem reveals a profound conflict between quantum mechanics and local realism, a conflict we reinterpret through the modern lens of causal inference. We propose and computationally validate a framework where quantum entanglement acts as a \"super-confounding\" resource, generating correlations that violate the classical causal bounds set by Bell's inequalities. This work makes three key contributions: First, we establish a physical hierarchy of confounding (Quantum \u003e Classical) and introduce Confounding Strength (CS) to quantify this effect. Second, we provide a circuit-based implementation of the quantum $\\mathcal{DO}$-calculus to distinguish causality from spurious correlation. Finally, we apply this calculus to a quantum machine learning problem, where causal feature selection yields a statistically significant 11.3% average absolute improvement in model robustness. Our framework bridges quantum foundations and causal AI, offering a new, practical perspective on quantum correlations.","short_abstract":"Bell's theorem reveals a profound conflict between quantum mechanics and local realism, a conflict we reinterpret through the modern lens of causal inference. We propose and computationally validate a framework where quantum entanglement acts as a \"super-confounding\" resource, generating correlations that violate the c...","url_abs":"https://arxiv.org/abs/2508.19327","url_pdf":"https://arxiv.org/pdf/2508.19327v1","authors":"[\"Pilsung Kang\"]","published":"2025-08-26T17:27:46Z","proceeding":"quant-ph","tasks":"[\"quant-ph\",\"cs.AI\",\"cs.LG\"]","methods":"[]","has_code":false}