Quantum framework for event graphs

• URL: http://arxiv.org/abs/2608.06058v2
• Date: Wed, 12 Aug 2026 22:15:34 GMT
• Title: Quantum framework for event graphs
• Abstract summary: We develop a quantum framework based on a directed participant graph.<n>The proposed framework establishes a mathematical foundation for quantum-inspired graph-based anomaly detection.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph representations of discrete events provide a natural foundation for machine-learning models of anomaly detection, yet they also suggest a deeper quantum description in which graph structure gives rise to interacting quantum degrees of freedom. We develop a quantum framework based on a directed participant graph whose edges represent events connecting pairs of source and destination vertices. A line-graph transformation maps each event to a node of a bidirectional event graph, whose edges inherit relational information from the participant graph. Since event datasets are naturally organized as collections of event records, their raw attributes align directly with the nodes of the event graph. A quantum harmonic oscillator (QHO) is assigned to every node of the participant graph, with the collective Hilbert space of these QHOs providing a complete basis for representing quantum states. Every directed edge of the participant graph thereby acquires a Schwinger isospin arising from the two endpoint oscillators. Under the line-graph transformation, event-graph nodes correspond to observable isospins whose interactions through bidirectional edges provide a natural substrate for learning from event datasets, while the quantum states associated with the underlying participant nodes remain latent and inaccessible to direct observation. Within this framework we formulate a compact U(1) lattice gauge theory (LGT) on the event graph that leads to a Kogut-Susskind Hamiltonian (KSH) in the form of an XY-type spin model governing the dynamics of sparse anomalous-event isospins immersed in a bath of many nominal events. The proposed framework establishes a mathematical foundation for quantum-inspired graph-based anomaly detection and provides a principled bridge between graph learning, LGT, and quantum information.

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