Interaction as Interference: A Quantum-Inspired Aggregation Approach

• URL: http://arxiv.org/abs/2511.10018v1
• Date: Fri, 14 Nov 2025 01:26:28 GMT
• Title: Interaction as Interference: A Quantum-Inspired Aggregation Approach
• Authors: Pilsung Kang,
• Abstract summary: We take a quantum-inspired view of interaction as engineered product terms or as emergent patterns in flexible models.<n>In a minimal linear-amplitude model, this cross-term equals the standard potential-outcome interaction contrast (_mathrmINT) in a factorial design.<n>We instantiate this idea in a lightweight emphInterference Kernel (IKC) and introduce two diagnostics: emphCoherent Gain (log-likelihood gain of coherent over the incoherent proxy) and emphInter
• Score: 4.07636450847048
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Classical approaches often treat interaction as engineered product terms or as emergent patterns in flexible models, offering little control over how synergy or antagonism arises. We take a quantum-inspired view: following the Born rule (probability as squared amplitude), \emph{coherent} aggregation sums complex amplitudes before squaring, creating an interference cross-term, whereas an \emph{incoherent} proxy sums squared magnitudes and removes it. In a minimal linear-amplitude model, this cross-term equals the standard potential-outcome interaction contrast \(Δ_{\mathrm{INT}}\) in a \(2\times 2\) factorial design, giving relative phase a direct, mechanism-level control over synergy versus antagonism. We instantiate this idea in a lightweight \emph{Interference Kernel Classifier} (IKC) and introduce two diagnostics: \emph{Coherent Gain} (log-likelihood gain of coherent over the incoherent proxy) and \emph{Interference Information} (the induced Kullback-Leibler gap). A controlled phase sweep recovers the identity. On a high-interaction synthetic task (XOR), IKC outperforms strong baselines under paired, budget-matched comparisons; on real tabular data (\emph{Adult} and \emph{Bank Marketing}) it is competitive overall but typically trails the most capacity-rich baseline in paired differences. Holding learned parameters fixed, toggling aggregation from incoherent to coherent consistently improves negative log-likelihood, Brier score, and expected calibration error, with positive Coherent Gain on both datasets.

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