CVMay 10, 2025

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

arXiv:2505.06516v11 citationsh-index: 42
Originality Incremental advance
AI Analysis

This work addresses conflict measurement in quantum-based decision-making for Out-of-Distribution detection, which is an incremental improvement in a domain-specific area.

The paper tackles the problem of managing conflicts between quantum mass functions in Quantum Dempster-Shafer Theory by proposing a Quantum Conflict Indicator (QCI) and applies it to Out-of-Distribution detection, achieving an average AUC increase of 1.2% and FPR95 decrease of 5.4% compared to baselines.

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.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes