Quantum Conflict Measurement in Decision Making for Out-of-Distribution Detection

2025-05-10

AI Summary

To address the challenge of quantifying and leveraging conflict among multiple quantum mass functions (QMFs) in quantum Dempster–Shafer theory (QDST), this paper proposes the first axiomatic quantum conflict indicator (QCI) satisfying non-negativity, symmetry, and boundedness—enabling interpretable measurement of decision-level conflict among QMFs. Building upon QCI, we design a conflict-aware fusion mechanism and introduce the C-DDS+ framework, which explicitly incorporates conflict information into out-of-distribution (OOD) detection. Experiments on mainstream OOD benchmarks demonstrate that C-DDS+ achieves an average AUC improvement of 1.2% and a 5.4% reduction in FPR95 over state-of-the-art methods. Our core contributions are twofold: (1) establishing the first axiomatic conflict quantification framework within QDST, and (2) pioneering the transformation of quantum conflict into discriminative signals for OOD detection.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Security and Privacy: Data transparency and provenance

Abstract

Quantum Dempster-Shafer Theory (QDST) uses quantum interference effects to derive a quantum mass function (QMF) as a fuzzy metric type from information obtained from various data sources. In addition, QDST uses quantum parallel computing to speed up computation. Nevertheless, the effective management of conflicts between multiple QMFs in QDST is a challenging question. This work aims to address this problem by proposing a Quantum Conflict Indicator (QCI) that measures the conflict between two QMFs in decision-making. Then, the properties of the QCI are carefully investigated. The obtained results validate its compliance with desirable conflict measurement properties such as non-negativity, symmetry, boundedness, extreme consistency and insensitivity to refinement. We then apply the proposed QCI in conflict fusion methods and compare its performance with several commonly used fusion approaches. This comparison demonstrates the superiority of the QCI-based conflict fusion method. Moreover, the Class Description Domain Space (C-DDS) and its optimized version, C-DDS+ by utilizing the QCI-based fusion method, are proposed to address the Out-of-Distribution (OOD) detection task. The experimental results show that the proposed approach gives better OOD performance with respect to several state-of-the-art baseline OOD detection methods. Specifically, it achieves an average increase in Area Under the Receiver Operating Characteristic Curve (AUC) of 1.2% and a corresponding average decrease in False Positive Rate at 95% True Negative Rate (FPR95) of 5.4% compared to the optimal baseline method.

Problem

• Measure conflict between quantum mass functions in QDST
• Propose Quantum Conflict Indicator for decision-making conflicts
• Enhance Out-of-Distribution detection using QCI-based fusion

Innovation

• Quantum Conflict Indicator measures QMF conflicts
• Quantum parallel computing accelerates QDST processing
• C-DDS+ enhances OOD detection via QCI fusion