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

Quantum Dempster-Shafer theory (QDST) derives a quantum mass function (QMF), a fuzzy metric obtained from multiple information sources based on quantum interference. In general, QMF effectively represents and processes uncertain information, but managing conflicts among multiple QMFs remains challenging. To address this issue, we propose a novel quantum conflict indicator (QCI) within the QDST framework. It is the first metric satisfying ideal conflict measurement properties, including non-negativity, symmetry, boundedness, extreme consistency, and insensitivity to refinement. Based on QCI, a novel quantum conflict fusion method (QCI-Fusion) is introduced to fuse highly conflicting QMFs. Moreover, traditional methods, including QCI-Fusion, typically constructs the Quantum Frame of Discernment (QFoD) based on predicted labels, which makes it difficult to cover unseen classes. Therefore, a new decision architecture, QCI-Decision, is proposed for unsupervised detection that rejects out-of-distribution (OOD) samples while maintaining in-distribution (ID) classification. Experimental results show that the classification accuracy of QCI-Decision deviates from the original model predictions by at most 1.49%. Meanwhile, compared with the latest OOD detection methods, QCI-Decision improves the Area Under the Receiver Operating Characteristic Curve (AUC) by up to 0.6% and reduces the False Positive Rate at 95% True Negative Rate (FPR) by up to 1.63%. Moreover, compared with QCI-Fusion, QCI-Decision achieves approximately threefold faster fusion speed with negligible performance degradation, offering a promising solution for open-world quantum information decision.